{"id":13404537,"url":"https://github.com/timzhang642/3D-Machine-Learning","last_synced_at":"2025-03-14T09:31:33.479Z","repository":{"id":37550179,"uuid":"100120455","full_name":"timzhang642/3D-Machine-Learning","owner":"timzhang642","description":"A resource repository for 3D machine learning","archived":false,"fork":false,"pushed_at":"2024-07-04T19:13:09.000Z","size":24851,"stargazers_count":9575,"open_issues_count":21,"forks_count":1770,"subscribers_count":604,"default_branch":"master","last_synced_at":"2024-07-31T19:44:13.212Z","etag":null,"topics":["3d","3d-reconstruction","constructive-solid-geometries","machine-learning","mesh","neural-networks","papers","point-cloud","primitives","voxel"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/timzhang642.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-08-12T15:20:54.000Z","updated_at":"2024-07-31T07:21:26.000Z","dependencies_parsed_at":"2024-10-26T08:17:35.080Z","dependency_job_id":null,"html_url":"https://github.com/timzhang642/3D-Machine-Learning","commit_stats":{"total_commits":262,"total_committers":53,"mean_commits":4.943396226415095,"dds":"0.40458015267175573","last_synced_commit":"c5bf0c3a47a586a9d368970df1364ab477632f36"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/timzhang642%2F3D-Machine-Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/timzhang642%2F3D-Machine-Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/timzhang642%2F3D-Machine-Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/timzhang642%2F3D-Machine-Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/timzhang642","download_url":"https://codeload.github.com/timzhang642/3D-Machine-Learning/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243554020,"owners_count":20309854,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["3d","3d-reconstruction","constructive-solid-geometries","machine-learning","mesh","neural-networks","papers","point-cloud","primitives","voxel"],"created_at":"2024-07-30T19:01:46.800Z","updated_at":"2025-03-14T09:31:31.841Z","avatar_url":"https://github.com/timzhang642.png","language":null,"readme":"3D Machine Learning\n\nIn recent years, tremendous amount of progress is being made in the field of 3D Machine Learning, which is an interdisciplinary field that fuses computer vision, computer graphics and machine learning. This repo is derived from my study notes and will be used as a place for triaging new research papers. \n\nI'll use the following icons to differentiate 3D representations:\n* :camera: Multi-view Images\n* :space_invader: Volumetric\n* :game_die: Point Cloud\n* :gem: Polygonal Mesh\n* :pill: Primitive-based\n\nTo find related papers and their relationships, check out [Connected Papers](https://www.connectedpapers.com/), which provides a neat way to visualize the academic field in a graph representation. \n\n## Get Involved\nTo contribute to this Repo, you may add content through pull requests or open an issue to let me know. \n\n:star:  :star:  :star:  :star:  :star:  :star:  :star:  :star:  :star:  :star:  :star:  :star:\u003cbr\u003e\nWe have also created a Slack workplace for people around the globe to ask questions, share knowledge and facilitate collaborations. Together, I'm sure we can advance this field as a collaborative effort. Join the community with [this link](https://join.slack.com/t/3d-machine-learning/shared_invite/zt-4hsgj8zb-G6OKrBcc17QBB9ppYETgCQ).\n\u003cbr\u003e:star:  :star:  :star:  :star:  :star:  :star:  :star:  :star:  :star:  :star:  :star:  :star:\n\n## Table of Contents\n- [Courses](#courses)\n- [Datasets](#datasets)\n  - [3D Models](#3d_models)\n  - [3D Scenes](#3d_scenes)\n- [3D Pose Estimation](#pose_estimation)\n- [Single Object Classification](#single_classification)\n- [Multiple Objects Detection](#multiple_detection)\n- [Scene/Object Semantic Segmentation](#segmentation)\n- [3D Geometry Synthesis/Reconstruction](#3d_synthesis)\n  - [Parametric Morphable Model-based methods](#3d_synthesis_model_based)\n  - [Part-based Template Learning methods](#3d_synthesis_template_based)\n  - [Deep Learning Methods](#3d_synthesis_dl_based)\n- [Texture/Material Analysis and Synthesis](#material_synthesis)\n- [Style Learning and Transfer](#style_transfer)\n- [Scene Synthesis/Reconstruction](#scene_synthesis)\n- [Scene Understanding](#scene_understanding)\n\n\u003ca name=\"courses\" /\u003e\n\n## Available Courses\n[Stanford CS231A: Computer Vision-From 3D Reconstruction to Recognition (Winter 2018)](http://web.stanford.edu/class/cs231a/)\n\n[UCSD CSE291-I00: Machine Learning for 3D Data (Winter 2018)](https://cse291-i.github.io/index.html)\n\n[Stanford CS468: Machine Learning for 3D Data (Spring 2017)](http://graphics.stanford.edu/courses/cs468-17-spring/)\n\n[MIT 6.838: Shape Analysis (Spring 2017)](http://groups.csail.mit.edu/gdpgroup/6838_spring_2017.html)\n\n[Princeton COS 526: Advanced Computer Graphics  (Fall 2010)](https://www.cs.princeton.edu/courses/archive/fall10/cos526/syllabus.php)\n\n[Princeton CS597: Geometric Modeling and Analysis (Fall 2003)](https://www.cs.princeton.edu/courses/archive/fall03/cs597D/)\n\n[Geometric Deep Learning](http://geometricdeeplearning.com/)\n\n[Paper Collection for 3D Understanding](https://www.cs.princeton.edu/courses/archive/spring15/cos598A/cos598A.html#Estimating)\n\n[CreativeAI: Deep Learning for Graphics](https://geometry.cs.ucl.ac.uk/workshops/creativeai/)\n\n\u003ca name=\"datasets\" /\u003e\n\n## Datasets\nTo see a survey of RGBD datasets, check out Michael Firman's [collection](http://www.michaelfirman.co.uk/RGBDdatasets/index.html) as well as the associated paper, [RGBD Datasets: Past, Present and Future](https://arxiv.org/pdf/1604.00999.pdf). Point Cloud Library also has a good dataset [catalogue](https://pointclouds.org/). \n\n\u003ca name=\"3d_models\" /\u003e\n\n### 3D Models\n\u003cb\u003ePrinceton Shape Benchmark (2003)\u003c/b\u003e [[Link]](http://shape.cs.princeton.edu/benchmark/)\n\u003cbr\u003e1,814 models collected from the web in .OFF format. Used to evaluating shape-based retrieval and analysis algorithms.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Princeton%20Shape%20Benchmark%20(2003).jpeg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eDataset for IKEA 3D models and aligned images (2013)\u003c/b\u003e [[Link]](http://ikea.csail.mit.edu/)\n\u003cbr\u003e759 images and 219 models including Sketchup (skp) and Wavefront (obj) files, good for pose estimation.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://ikea.csail.mit.edu/web_img/ikea_object.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eOpen Surfaces: A Richly Annotated Catalog of Surface Appearance (SIGGRAPH 2013)\u003c/b\u003e [[Link]](http://opensurfaces.cs.cornell.edu/)\n\u003cbr\u003eOpenSurfaces is a large database of annotated surfaces created from real-world consumer photographs. Our annotation framework draws on crowdsourcing to segment surfaces from photos, and then annotate them with rich surface properties, including material, texture and contextual information.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://opensurfaces.cs.cornell.edu/static/img/teaser4-web.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003ePASCAL3D+ (2014)\u003c/b\u003e [[Link]](http://cvgl.stanford.edu/projects/pascal3d.html)\n\u003cbr\u003e12 categories, on average 3k+ objects per category, for 3D object detection and pose estimation.\n\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://cvgl.stanford.edu/projects/pascal3d+/pascal3d.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eModelNet (2015)\u003c/b\u003e [[Link]](http://modelnet.cs.princeton.edu/#)\n\u003cbr\u003e127915 3D CAD models from 662 categories\n\u003cbr\u003eModelNet10: 4899 models from 10 categories\n\u003cbr\u003eModelNet40: 12311 models from 40 categories, all are uniformly orientated\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://3dvision.princeton.edu/projects/2014/ModelNet/thumbnail.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eShapeNet (2015)\u003c/b\u003e [[Link]](https://www.shapenet.org/)\n\u003cbr\u003e3Million+ models and 4K+ categories. A dataset that is large in scale, well organized and richly annotated.\n\u003cbr\u003eShapeNetCore [[Link]](http://shapenet.cs.stanford.edu/shrec16/): 51300 models for 55 categories.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://msavva.github.io/files/shapenet.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eA Large Dataset of Object Scans (2016)\u003c/b\u003e [[Link]](http://redwood-data.org/3dscan/index.html)\n\u003cbr\u003e10K scans in RGBD + reconstructed 3D models in .PLY format.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://redwood-data.org/3dscan/img/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eObjectNet3D: A Large Scale Database for 3D Object Recognition (2016)\u003c/b\u003e [[Link]](http://cvgl.stanford.edu/projects/objectnet3d/)\n\u003cbr\u003e100 categories, 90,127 images, 201,888 objects in these images and 44,147 3D shapes. \n\u003cbr\u003eTasks: region proposal generation, 2D object detection, joint 2D detection and 3D object pose estimation, and image-based 3D shape retrieval\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://cvgl.stanford.edu/projects/objectnet3d/ObjectNet3D.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eThingi10K: A Dataset of 10,000 3D-Printing Models (2016)\u003c/b\u003e [[Link]](https://ten-thousand-models.appspot.com/)\n\u003cbr\u003e10,000 models from featured “things” on thingiverse.com, suitable for testing 3D printing techniques such as structural analysis , shape optimization, or solid geometry operations.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://pbs.twimg.com/media/DRbxWnqXkAEEH0g.jpg:large\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eABC: A Big CAD Model Dataset For Geometric Deep Learning\u003c/b\u003e [[Link]](https://cs.nyu.edu/~zhongshi/publication/abc-dataset/)[[Paper]](https://arxiv.org/abs/1812.06216)\n\u003cbr\u003eThis work introduce a dataset for geometric deep learning consisting of over 1 million individual (and high quality) geometric models, each associated with accurate ground truth information on the decomposition into patches, explicit sharp feature annotations, and analytic differential properties.\u003cbr\u003e\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://cs.nyu.edu/~zhongshi/img/abc-dataset.png\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003eScanObjectNN: A New Benchmark Dataset and Classification Model on Real-World Data (ICCV 2019)\u003c/b\u003e [[Link]](https://hkust-vgd.github.io/scanobjectnn/)\n\u003cbr\u003e\nThis work introduce ScanObjectNN, a new real-world point cloud object dataset based on scanned indoor scene data. The comprehensive benchmark in this work shows that this dataset poses great challenges to existing point cloud classification techniques as objects from real-world scans are often cluttered with background and/or are partial due to occlusions. Three key open problems for point cloud object classification are identified, and a new point cloud classification neural network that achieves state-of-the-art performance on classifying objects with cluttered background is proposed.\n\u003cbr\u003e\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://hkust-vgd.github.io/scanobjectnn/images/objects_teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eVOCASET: Speech-4D Head Scan Dataset (2019(\u003c/b\u003e [[Link]](https://voca.is.tue.mpg.de/)[[Paper]](https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/510/paper_final.pdf)\n\u003cbr\u003e[VOCASET](https://voca.is.tue.mpg.de/), is a 4D face dataset with about 29 minutes of 4D scans captured at 60 fps and synchronized audio. The dataset has 12 subjects and 480 sequences of about 3-4 seconds each with sentences chosen from an array of standard protocols that maximize  phonetic  diversity. \n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/TimoBolkart/voca/blob/master/gif/vocaset.gif\" /\u003e\u003c/p\u003e\n\n\u003cb\u003e3D-FUTURE: 3D FUrniture shape with TextURE (2020)\u003c/b\u003e [[Link]](https://tianchi.aliyun.com/specials/promotion/alibaba-3d-future?spm=5176.14208320.0.0.66293cf7asRnrR)\n\u003cbr\u003e[3D-FUTURE](https://tianchi.aliyun.com/specials/promotion/alibaba-3d-future) contains 20,000+ clean and realistic synthetic scenes in 5,000+ diverse rooms, which include 10,000+ unique high quality 3D instances of furniture with high resolution informative textures developed by professional designers. \n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://img.alicdn.com/tfs/TB1HTSfz4v1gK0jSZFFXXb0sXXa-1999-1037.png\" /\u003e\u003c/p\u003e\n\n\n\u003cb\u003eFusion 360 Gallery Dataset (2020)\u003c/b\u003e [[Link]](https://github.com/AutodeskAILab/Fusion360GalleryDataset)[[Paper]](https://arxiv.org/abs/2010.02392)\n\u003cbr\u003eThe [Fusion 360 Gallery Dataset](https://github.com/AutodeskAILab/Fusion360GalleryDataset) contains rich 2D and 3D geometry data derived from parametric CAD models. The Reconstruction Dataset provides sequential construction sequence information from a subset of simple 'sketch and extrude' designs. The Segmentation Dataset provides a segmentation of 3D models based on the CAD modeling operation, including B-Rep format, mesh, and point cloud.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://raw.githubusercontent.com/AutodeskAILab/Fusion360GalleryDataset/master/docs/images/reconstruction_teaser.jpg\" /\u003e\n\u003cimg width=\"50%\" src=\"https://raw.githubusercontent.com/AutodeskAILab/Fusion360GalleryDataset/master/docs/images/segmentation_example.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eMechanical Components Benchmark (2020)\u003c/b\u003e[[Link]](https://mechanical-components.herokuapp.com)[[Paper]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123630171.pdf)\n\u003cbr\u003e[MCB](https://mechanical-components.herokuapp.com) is a large-scale dataset of 3D objects of mechanical components. It has a total number of 58,696 mechanical components with 68 classes.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://mechanical-components.herokuapp.com/static/img/main_figure.png\" /\u003e\n\u003c/p\u003e\n\n\u003cb\u003eCombinatorial 3D Shape Dataset (2020)\u003c/b\u003e [[Link]](https://github.com/POSTECH-CVLab/Combinatorial-3D-Shape-Generation)[[Paper]](https://arxiv.org/abs/2004.07414)\n\u003cbr\u003e[Combinatorial 3D Shape Dataset](https://github.com/POSTECH-CVLab/Combinatorial-3D-Shape-Generation) is composed of 406 instances of 14 classes. Each object in our dataset is considered equivalent to a sequence of primitive placement. Compared to other 3D object datasets, our proposed dataset contains an assembling sequence of unit primitives. It implies that we can quickly obtain a sequential generation process that is a human assembling mechanism. Furthermore, we can sample valid random sequences from a given combinatorial shape after validating the sampled sequences. To sum up, the characteristics of our combinatorial 3D shape dataset are (i) combinatorial, (ii) sequential, (iii) decomposable, and (iv) manipulable.\n\u003cp align=\"center\"\u003e\n\u003cimg width=\"65%\" src=\"imgs/combinatorial_3d_shape_dataset.png\" /\u003e\n\u003c/p\u003e\n\n\u003ca name=\"3d_scenes\" /\u003e\n\n### 3D Scenes\n\u003cb\u003eNYU Depth Dataset V2 (2012)\u003c/b\u003e [[Link]](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html)\n\u003cbr\u003e1449 densely labeled pairs of aligned RGB and depth images from Kinect video sequences for a variety of indoor scenes.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://cs.nyu.edu/~silberman/images/nyu_depth_v2_labeled.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eSUNRGB-D 3D Object Detection Challenge\u003c/b\u003e [[Link]](http://rgbd.cs.princeton.edu/challenge.html)\n\u003cbr\u003e19 object categories for predicting a 3D bounding box in real world dimension\n\u003cbr\u003eTraining set: 10,355 RGB-D scene images, Testing set: 2860 RGB-D images\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://rgbd.cs.princeton.edu/3dbox.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eSceneNN (2016)\u003c/b\u003e [[Link]](http://www.scenenn.net/)\n\u003cbr\u003e100+ indoor scene meshes with per-vertex and per-pixel annotation.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://cdn-ak.f.st-hatena.com/images/fotolife/r/robonchu/20170611/20170611155625.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eScanNet (2017)\u003c/b\u003e [[Link]](http://www.scan-net.org/)\n\u003cbr\u003eAn RGB-D video dataset containing 2.5 million views in more than 1500 scans, annotated with 3D camera poses, surface reconstructions, and instance-level semantic segmentations.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://www.scan-net.org/img/annotations.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eMatterport3D: Learning from RGB-D Data in Indoor Environments (2017)\u003c/b\u003e [[Link]](https://niessner.github.io/Matterport/)\n\u003cbr\u003e10,800 panoramic views (in both RGB and depth) from 194,400 RGB-D images of 90 building-scale scenes of private rooms. Instance-level semantic segmentations are provided for region (living room, kitchen) and object (sofa, TV) categories. \n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://niessner.github.io/Matterport/teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eSUNCG: A Large 3D Model Repository for Indoor Scenes (2017)\u003c/b\u003e [[Link]](http://suncg.cs.princeton.edu/)\n\u003cbr\u003eThe dataset contains over 45K different scenes with manually created realistic room and furniture layouts. All of the scenes are semantically annotated at the object level.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://suncg.cs.princeton.edu/figures/data_full.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eMINOS: Multimodal Indoor Simulator (2017)\u003c/b\u003e [[Link]](https://github.com/minosworld/minos)\n\u003cbr\u003eMINOS is a simulator designed to support the development of multisensory models for goal-directed navigation in complex indoor environments. MINOS leverages large datasets of complex 3D environments and supports flexible configuration of multimodal sensor suites. MINOS supports SUNCG and Matterport3D scenes.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://vladlen.info/wp-content/uploads/2017/12/MINOS.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eFacebook House3D: A Rich and Realistic 3D Environment (2017)\u003c/b\u003e [[Link]](https://github.com/facebookresearch/House3D)\n\u003cbr\u003eHouse3D is a virtual 3D environment which consists of 45K indoor scenes equipped with a diverse set of scene types, layouts and objects sourced from the SUNCG dataset. All 3D objects are fully annotated with category labels. Agents in the environment have access to observations of multiple modalities, including RGB images, depth, segmentation masks and top-down 2D map views.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://user-images.githubusercontent.com/1381301/33509559-87c4e470-d6b7-11e7-8266-27c940d5729a.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eHoME: a Household Multimodal Environment (2017)\u003c/b\u003e [[Link]](https://home-platform.github.io/)\n\u003cbr\u003eHoME integrates over 45,000 diverse 3D house layouts based on the SUNCG dataset, a scale which may facilitate learning, generalization, and transfer. HoME is an open-source, OpenAI Gym-compatible platform extensible to tasks in reinforcement learning, language grounding, sound-based navigation, robotics, multi-agent learning.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://home-platform.github.io/assets/overview.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eAI2-THOR: Photorealistic Interactive Environments for AI Agents\u003c/b\u003e [[Link]](http://ai2thor.allenai.org/)\n\u003cbr\u003eAI2-THOR is a photo-realistic interactable framework for AI agents. There are a total 120 scenes in version 1.0 of the THOR environment covering four different room categories: kitchens, living rooms, bedrooms, and bathrooms. Each room has a number of actionable objects.\n\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/AI2-Thor.jpeg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eUnrealCV: Virtual Worlds for Computer Vision (2017)\u003c/b\u003e [[Link]](http://unrealcv.org/)[[Paper]](http://www.idm.pku.edu.cn/staff/wangyizhou/papers/ACMMM2017_UnrealCV.pdf)\n\u003cbr\u003eAn open source project to help computer vision researchers build virtual worlds using Unreal Engine 4.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://unrealcv.org/images/homepage_teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eGibson Environment: Real-World Perception for Embodied Agents (2018 CVPR) \u003c/b\u003e [[Link]](http://gibsonenv.stanford.edu/)\n\u003cbr\u003eThis platform provides RGB from 1000 point clouds, as well as multimodal sensor data: surface normal, depth, and for a fraction of the spaces, semantics object annotations. The environment is also RL ready with physics integrated. Using such datasets can further narrow down the discrepency between virtual environment and real world.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Gibson%20Environment-%20Real-World%20Perception%20for%20Embodied%20Agents%20(2018%20CVPR)%20.jpeg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eInteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset\u003c/b\u003e [[Link]](https://interiornet.org/)\n\u003cbr\u003eSystem Overview: an end-to-end pipeline to render an RGB-D-inertial benchmark for large scale interior scene understanding and mapping. Our dataset contains 20M images created by pipeline: (A) We collect around 1 million CAD models provided by world-leading furniture manufacturers. These models have been used in the real-world production. (B) Based on those models, around 1,100 professional designers create around 22 million interior layouts. Most of such layouts have been used in real-world decorations. (C) For each layout, we generate a number of configurations to represent different random lightings and simulation of scene change over time in daily life. (D) We provide an interactive simulator (ViSim) to help for creating ground truth IMU, events, as well as monocular or stereo camera trajectories including hand-drawn, random walking and neural network based realistic trajectory. (E) All supported image sequences and ground truth.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://interiornet.org/items/InteriorNet.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eSemantic3D\u003c/b\u003e[[Link]](http://www.semantic3d.net/)\n\u003cbr\u003eLarge-Scale Point Cloud Classification Benchmark, which provides a large labelled 3D point cloud data set of natural scenes with over 4 billion points in total, and also covers a range of diverse urban scenes.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://www.semantic3d.net/img/full_resolution/sg27_8.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eStructured3D: A Large Photo-realistic Dataset for Structured 3D Modeling\u003c/b\u003e [[Link]](https://structured3d-dataset.org/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://structured3d-dataset.org/static/img/teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003e3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics\u003c/b\u003e [[Link]](https://tianchi.aliyun.com/specials/promotion/alibaba-3d-scene-dataset)\n\u003cbr\u003eContains 10,000 houses (or apartments) and ~70,000 rooms with layout information. \n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://img.alicdn.com/tfs/TB131XOJeL2gK0jSZPhXXahvXXa-2992-2751.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003e3ThreeDWorld(TDW): A High-Fidelity, Multi-Modal Platform for Interactive Physical Simulation\u003c/b\u003e [[Link]](http://www.threedworld.org/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://www.threedworld.org/img/gallery/gallery-1.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eMINERVAS: Massive INterior EnviRonments VirtuAl Synthesis\u003c/b\u003e [[Link]](https://coohom.github.io/MINERVAS/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://coohom.github.io/MINERVAS/static/img/teaser.png\" /\u003e\u003c/p\u003e\n\n\u003ca name=\"pose_estimation\" /\u003e\n\n## 3D Pose Estimation\n\u003cb\u003eCategory-Specific Object Reconstruction from a Single Image (2014)\u003c/b\u003e [[Paper]](https://people.eecs.berkeley.edu/~akar/categoryshapes.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://people.eecs.berkeley.edu/~akar/basisshapes_highres.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eViewpoints and Keypoints (2015)\u003c/b\u003e [[Paper]](https://people.eecs.berkeley.edu/~shubhtuls/papers/cvpr15vpsKps.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Viewpoints%20and%20Keypoints.jpeg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eRender for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views (2015 ICCV)\u003c/b\u003e [[Paper]](https://shapenet.cs.stanford.edu/projects/RenderForCNN/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://shapenet.cs.stanford.edu/projects/RenderForCNN/images/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003ePoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization (2015)\u003c/b\u003e [[Paper]](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Kendall_PoseNet_A_Convolutional_ICCV_2015_paper.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://mi.eng.cam.ac.uk/projects/relocalisation/images/map.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eModeling Uncertainty in Deep Learning for Camera Relocalization (2016)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1509.05909.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Modeling%20Uncertainty%20in%20Deep%20Learning%20for%20Camera%20Relocalization.jpeg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eRobust camera pose estimation by viewpoint classification using deep learning (2016)\u003c/b\u003e [[Paper]](https://link.springer.com/article/10.1007/s41095-016-0067-z)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Robust%20camera%20pose%20estimation%20by%20viewpoint%20classification%20using%20deep%20learning.jpeg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eImage-based localization using lstms for structured feature correlation (2017 ICCV)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1611.07890.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"./imgs/Image-based localization using LSTMs for structured feature correlation.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eImage-Based Localization Using Hourglass Networks (2017 ICCV Workshops)\u003c/b\u003e [[Paper]](https://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w17/Melekhov_Image-Based_Localization_Using_ICCV_2017_paper.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"./imgs/Image-Based Localization Using Hourglass Networks.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eGeometric loss functions for camera pose regression with deep learning (2017 CVPR)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1704.00390.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://mi.eng.cam.ac.uk/~cipolla/images/pose-net.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eGeneric 3D Representation via Pose Estimation and Matching (2017)\u003c/b\u003e [[Paper]](http://3drepresentation.stanford.edu/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Generic%203D%20Representation%20via%20Pose%20Estimation%20and%20Matching.jpeg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003e3D Bounding Box Estimation Using Deep Learning and Geometry (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1612.00496.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/3D%20Bounding%20Box%20Estimation%20Using%20Deep%20Learning%20and%20Geometry.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003e6-DoF Object Pose from Semantic Keypoints (2017)\u003c/b\u003e [[Paper]](https://www.seas.upenn.edu/~pavlakos/projects/object3d/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://www.seas.upenn.edu/~pavlakos/projects/object3d/files/object3d-teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eRelative Camera Pose Estimation Using Convolutional Neural Networks (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1702.01381.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Relative%20Camera%20Pose%20Estimation%20Using%20Convolutional%20Neural%20Networks.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003e3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions (2017)\u003c/b\u003e [[Paper]](http://3dmatch.cs.princeton.edu/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://3dmatch.cs.princeton.edu/img/overview.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eSingle Image 3D Interpreter Network (2016)\u003c/b\u003e [[Paper]](http://3dinterpreter.csail.mit.edu/) [[Code]](https://github.com/jiajunwu/3dinn)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://3dinterpreter.csail.mit.edu/images/spotlight_3dinn_large.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eMulti-view Consistency as Supervisory Signal  for Learning Shape and Pose Prediction (2018 CVPR)\u003c/b\u003e [[Paper]](https://shubhtuls.github.io/mvcSnP/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://shubhtuls.github.io/mvcSnP/resources/images/teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003ePoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes (2018)\u003c/b\u003e [[Paper]](https://rse-lab.cs.washington.edu/projects/posecnn/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://yuxng.github.io/PoseCNN.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eFeature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images (2018 CVPR)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1712.03904.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"40%\" src=\"https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTnpyajEhbhrPMc0YpEQzqE8N9E7CW_EVWYA3Bxg46oUEYFf9XvkA\" /\u003e\u003c/p\u003e\n\n\u003cb\u003ePix3D: Dataset and Methods for Single-Image 3D Shape Modeling (2018 CVPR)\u003c/b\u003e [[Paper]](http://pix3d.csail.mit.edu/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://pix3d.csail.mit.edu/images/spotlight_pix3d.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003e3D Pose Estimation and 3D Model Retrieval for Objects in the Wild (2018 CVPR)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1803.11493.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://www.tugraz.at/fileadmin/user_upload/Institute/ICG/Documents/team_lepetit/images/grabner/pose_retrieval_overview.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eDeep Object Pose Estimation for Semantic Robotic Grasping of Household Objects (2018)\u003c/b\u003e [[Paper]](https://research.nvidia.com/publication/2018-09_Deep-Object-Pose)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://research.nvidia.com/sites/default/files/publications/forwebsite1_0.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eMocapNET2: a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format (2021)\u003c/b\u003e [[Paper]](http://users.ics.forth.gr/~argyros/mypapers/2021_01_ICPR_Qammaz.pdf), [[Code]](https://github.com/FORTH-ModelBasedTracker/MocapNET)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://raw.githubusercontent.com/FORTH-ModelBasedTracker/MocapNET/master/doc/mnet2.png\" /\u003e\u003c/p\u003e\n\n\u003ca name=\"single_classification\" /\u003e\n\n## Single Object Classification\n:space_invader: \u003cb\u003e3D ShapeNets: A Deep Representation for Volumetric Shapes (2015)\u003c/b\u003e [[Paper]](http://3dshapenets.cs.princeton.edu/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/3ed23386284a5639cb3e8baaecf496caa766e335/1-Figure1-1.png\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eVoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition (2015)\u003c/b\u003e [[Paper]](http://www.dimatura.net/publications/voxnet_maturana_scherer_iros15.pdf) [[Code]](https://github.com/dimatura/voxnet)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://www.dimatura.net/research/voxnet/car_voxnet_side.png\" /\u003e\u003c/p\u003e\n\n:camera: \u003cb\u003eMulti-view Convolutional Neural Networks  for 3D Shape Recognition (2015)\u003c/b\u003e [[Paper]](http://vis-www.cs.umass.edu/mvcnn/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://vis-www.cs.umass.edu/mvcnn/images/mvcnn.png\" /\u003e\u003c/p\u003e\n\n:camera: \u003cb\u003eDeepPano: Deep Panoramic Representation for 3-D Shape Recognition (2015)\u003c/b\u003e [[Paper]](http://mclab.eic.hust.edu.cn/UpLoadFiles/Papers/DeepPano_SPL2015.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"30%\" src=\"https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/5a1b5d31905d8cece7b78510f51f3d8bbb063063/1-Figure3-1.png\" /\u003e\u003c/p\u003e\n\n:space_invader::camera: \u003cb\u003eFusionNet: 3D Object Classification Using Multiple Data Representations (2016)\u003c/b\u003e [[Paper]](https://stanford.edu/~rezab/papers/fusionnet.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"30%\" src=\"https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/0aab8fbcef1f0a14f5653d170ca36f4e5aae8010/6-Figure5-1.png\" /\u003e\u003c/p\u003e\n\n:space_invader::camera: \u003cb\u003eVolumetric and Multi-View CNNs for Object Classification on 3D Data (2016)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1604.03265.pdf) [[Code]](https://github.com/charlesq34/3dcnn.torch)\n\u003cp align=\"center\"\u003e\u003cimg width=\"40%\" src=\"http://graphics.stanford.edu/projects/3dcnn/teaser.jpg\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eGenerative and Discriminative Voxel Modeling with Convolutional Neural Networks (2016)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1608.04236.pdf) [[Code]](https://github.com/ajbrock/Generative-and-Discriminative-Voxel-Modeling)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://davidstutz.de/wordpress/wp-content/uploads/2017/02/brock_vae.png\" /\u003e\u003c/p\u003e\n\n:gem: \u003cb\u003eGeometric deep learning on graphs and manifolds using mixture model CNNs (2016)\u003c/b\u003e [[Link]](https://arxiv.org/pdf/1611.08402.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://i2.wp.com/preferredresearch.jp/wp-content/uploads/2017/08/monet.png?resize=581%2C155\u0026ssl=1\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003e3D GAN: Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling (2016)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1610.07584.pdf) [[Code]](https://github.com/zck119/3dgan-release)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://3dgan.csail.mit.edu/images/model.jpg\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eGenerative and Discriminative Voxel Modeling with Convolutional Neural Networks (2017)\u003c/b\u003e [[Paper]](https://github.com/ajbrock/Generative-and-Discriminative-Voxel-Modeling)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/ajbrock/Generative-and-Discriminative-Voxel-Modeling/blob/master/doc/GUI3.png\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eFPNN: Field Probing Neural Networks for 3D Data (2016)\u003c/b\u003e [[Paper]](http://yangyanli.github.io/FPNN/) [[Code]](https://github.com/yangyanli/FPNN)\n\u003cp align=\"center\"\u003e\u003cimg width=\"30%\" src=\"https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/15ca7adccf5cd4dc309cdcaa6328f4c429ead337/1-Figure2-1.png\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eOctNet: Learning Deep 3D Representations at High Resolutions (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1611.05009.pdf) [[Code]](https://github.com/griegler/octnet)\n\u003cp align=\"center\"\u003e\u003cimg width=\"30%\" src=\"https://is.tuebingen.mpg.de/uploads/publication/image/18921/img03.png\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eO-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis (2017)\u003c/b\u003e [[Paper]](http://wang-ps.github.io/O-CNN) [[Code]](https://github.com/Microsoft/O-CNN)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://wang-ps.github.io/O-CNN_files/teaser.png\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eOrientation-boosted voxel nets for 3D object recognition (2017)\u003c/b\u003e [[Paper]](https://lmb.informatik.uni-freiburg.de/Publications/2017/SZB17a/) [[Code]](https://github.com/lmb-freiburg/orion)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://lmb.informatik.uni-freiburg.de/Publications/2017/SZB17a/teaser_w.png\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003ePointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (2017)\u003c/b\u003e [[Paper]](http://stanford.edu/~rqi/pointnet/) [[Code]](https://github.com/charlesq34/pointnet)\n\u003cp align=\"center\"\u003e\u003cimg width=\"40%\" src=\"https://web.stanford.edu/~rqi/papers/pointnet.png\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003ePointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1706.02413.pdf) [[Code]](https://github.com/charlesq34/pointnet2)\n\u003cp align=\"center\"\u003e\u003cimg width=\"40%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/PointNet%2B%2B-%20Deep%20Hierarchical%20Feature%20Learning%20on%20Point%20Sets%20in%20a%20Metric%20Space.png\" /\u003e\u003c/p\u003e\n\n:camera: \u003cb\u003eFeedback Networks (2017)\u003c/b\u003e [[Paper]](http://feedbacknet.stanford.edu/) [[Code]](https://github.com/amir32002/feedback-networks)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Feedback%20Networks.png\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003eEscape from Cells: Deep Kd-Networks for The Recognition of 3D Point Cloud Models (2017)\u003c/b\u003e [[Paper]](http://www.arxiv.org/pdf/1704.01222.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Escape From Cells.png\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003eDynamic Graph CNN for Learning on Point Clouds (2018)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1801.07829.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://liuziwei7.github.io/homepage_files/dynamicgcnn_logo.png\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003ePointCNN (2018)\u003c/b\u003e [[Paper]](https://yangyanli.github.io/PointCNN/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://yangyan.li/images/paper/pointcnn.png\" /\u003e\u003c/p\u003e\n\n:game_die::camera: \u003cb\u003eA Network Architecture for Point Cloud Classification via Automatic Depth Images Generation (2018 CVPR)\u003c/b\u003e [[Paper]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Roveri_A_Network_Architecture_CVPR_2018_paper.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://s3-us-west-1.amazonaws.com/disneyresearch/wp-content/uploads/20180619114732/A-Network-Architecture-for-Point-Cloud-Classification-via-Automatic-Depth-Images-Generation-Image-600x317.jpg\" /\u003e\u003c/p\u003e\n\n:game_die::space_invader: \u003cb\u003ePointGrid: A Deep Network for 3D Shape Understanding (CVPR 2018) \u003c/b\u003e [[Paper]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Le_PointGrid_A_Deep_CVPR_2018_paper.pdf) [[Code]](https://github.com/trucleduc/PointGrid)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/PointGrid-%20A%20Deep%20Network%20for%203D%20Shape%20Understanding%20(2018).jpeg\" /\u003e\u003c/p\u003e\n\n:gem: \u003cb\u003e MeshNet: Mesh Neural Network for 3D Shape Representation (AAAI 2019) \u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1811.11424.pdf) [[Code]](https://github.com/Yue-Group/MeshNet)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://www.gaoyue.org/en_tsinghua/resrc/meshnet.jpg\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003eSpiderCNN (2018)\u003c/b\u003e [[Paper]](https://github.com/xyf513/SpiderCNN)[[Code]](https://github.com/xyf513/SpiderCNN)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://5b0988e595225.cdn.sohucs.com/images/20181109/45c3b670e67f43b288791c650fb7fb0b.jpeg\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003ePointConv (2018)\u003c/b\u003e [[Paper]](https://github.com/DylanWusee/pointconv/tree/master/imgs)[[Code]](https://github.com/DylanWusee/pointconv/tree/master/imgs)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://pics4.baidu.com/feed/8b82b9014a90f603272fe29f88ef061fb251ed49.jpeg?token=b23e1dbbaeaf12ffe3d168bd997a8d66\u0026s=01307D328FE07C010C69C1CE0000D0B3\" /\u003e\u003c/p\u003e\n\n:gem: \u003cb\u003eMeshCNN (SIGGRAPH 2019)\u003c/b\u003e [[Paper]](https://bit.ly/meshcnn)[[Code]](https://github.com/ranahanocka/MeshCNN)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/ranahanocka/MeshCNN/blob/master/docs/imgs/alien.gif?raw=true\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003eSampleNet: Differentiable Point Cloud Sampling (CVPR 2020)\u003c/b\u003e [[Paper]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Lang_SampleNet_Differentiable_Point_Cloud_Sampling_CVPR_2020_paper.pdf) [[Code]](https://github.com/itailang/SampleNet)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/itailang/SampleNet/blob/master/doc/teaser.png\" /\u003e\u003c/p\u003e\n\n\u003ca name=\"multiple_detection\" /\u003e\n\n\n## Multiple Objects Detection\n\u003cb\u003eSliding Shapes for 3D Object Detection in Depth Images (2014)\u003c/b\u003e [[Paper]](http://slidingshapes.cs.princeton.edu/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://slidingshapes.cs.princeton.edu/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eObject Detection in 3D Scenes Using CNNs in Multi-view Images (2016)\u003c/b\u003e [[Paper]](https://stanford.edu/class/ee367/Winter2016/Qi_Report.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Object%20Detection%20in%203D%20Scenes%20Using%20CNNs%20in%20Multi-view%20Images.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eDeep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images (2016)\u003c/b\u003e [[Paper]](http://dss.cs.princeton.edu/) [[Code]](https://github.com/shurans/DeepSlidingShape)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://3dvision.princeton.edu/slide/DSS.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eThree-Dimensional Object Detection and Layout Prediction using Clouds of Oriented Gradients (2016)\u003c/b\u003e [[CVPR '16 Paper]](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Ren_Three-Dimensional_Object_Detection_CVPR_2016_paper.pdf) [[CVPR '18 Paper]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Ren_3D_Object_Detection_CVPR_2018_paper.pdf) [[T-PAMI '19 Paper]](https://arxiv.org/pdf/1906.04725) \n\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/luvegood/3D-Machine-Learning/blob/master/imgs/Three-Dimensional%20Object%20Detection%20and%20Layout%20Prediction%20using%20Clouds%20of%20Oriented%20Gradients.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eDeepContext: Context-Encoding Neural Pathways  for 3D Holistic Scene Understanding (2016)\u003c/b\u003e [[Paper]](http://deepcontext.cs.princeton.edu/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://deepcontext.cs.princeton.edu/teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eSUN RGB-D: A RGB-D Scene Understanding Benchmark Suite (2017)\u003c/b\u003e [[Paper]](http://rgbd.cs.princeton.edu/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://rgbd.cs.princeton.edu/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eVoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1711.06396.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://pbs.twimg.com/media/DPMtLhHXUAcQUj2.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eFrustum PointNets for 3D Object Detection from RGB-D Data (CVPR2018)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1711.08488.pdf)\n\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://stanford.edu/~rqi/frustum-pointnets/images/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eA^2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes (AAAI2019)\u003c/b\u003e [[Paper]](https://arxiv.org/abs/1901.00785)\n\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"imgs/a-square-net-min.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eStereo R-CNN based 3D Object Detection for Autonomous Driving (CVPR2019)\u003c/b\u003e [[Paper]](https://arxiv.org/abs/1902.09738v1)\n\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://www.groundai.com/media/arxiv_projects/515338/system_newnew.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eDeep Hough Voting for 3D Object Detection in Point Clouds (ICCV2019)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1904.09664.pdf) [[code]](https://github.com/facebookresearch/votenet)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/facebookresearch/votenet/blob/master/doc/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003ca name=\"segmentation\" /\u003e\n\n## Scene/Object Semantic Segmentation\n\u003cb\u003eLearning 3D Mesh Segmentation and Labeling (2010)\u003c/b\u003e [[Paper]](https://people.cs.umass.edu/~kalo/papers/LabelMeshes/LabelMeshes.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/0bf390e2a14f74bcc8838d5fb1c0c4cc60e92eb7/7-Figure7-1.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eUnsupervised Co-Segmentation of a Set of Shapes via Descriptor-Space Spectral Clustering (2011)\u003c/b\u003e [[Paper]](https://www.cs.sfu.ca/~haoz/pubs/sidi_siga11_coseg.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"30%\" src=\"http://people.scs.carleton.ca/~olivervankaick/cosegmentation/results6.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eSingle-View Reconstruction via Joint Analysis of Image and Shape Collections (2015)\u003c/b\u003e [[Paper]](https://www.cs.utexas.edu/~huangqx/modeling_sig15.pdf) [[Code]](https://github.com/huangqx/image_shape_align)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://vladlen.info/wp-content/uploads/2015/05/single-view.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003e3D Shape Segmentation with Projective Convolutional Networks (2017)\u003c/b\u003e [[Paper]](http://people.cs.umass.edu/~kalo/papers/shapepfcn/) [[Code]](https://github.com/kalov/ShapePFCN)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://people.cs.umass.edu/~kalo/papers/shapepfcn/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eLearning Hierarchical Shape Segmentation and Labeling from Online Repositories (2017)\u003c/b\u003e [[Paper]](http://cs.stanford.edu/~ericyi/project_page/hier_seg/index.html)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://cs.stanford.edu/~ericyi/project_page/hier_seg/figures/teaser.jpg\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eScanNet (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1702.04405.pdf) [[Code]](https://github.com/scannet/scannet)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://www.scan-net.org/img/voxel-predictions.jpg\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003ePointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (2017)\u003c/b\u003e [[Paper]](http://stanford.edu/~rqi/pointnet/) [[Code]](https://github.com/charlesq34/pointnet)\n\u003cp align=\"center\"\u003e\u003cimg width=\"40%\" src=\"https://web.stanford.edu/~rqi/papers/pointnet.png\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003ePointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1706.02413.pdf) [[Code]](https://github.com/charlesq34/pointnet2)\n\u003cp align=\"center\"\u003e\u003cimg width=\"40%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/PointNet%2B%2B-%20Deep%20Hierarchical%20Feature%20Learning%20on%20Point%20Sets%20in%20a%20Metric%20Space.png\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003e3D Graph Neural Networks for RGBD Semantic Segmentation (2017)\u003c/b\u003e [[Paper]](http://www.cs.toronto.edu/~rjliao/papers/iccv_2017_3DGNN.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"40%\" src=\"http://www.fonow.com/Images/2017-10-18/66372-20171018115809740-2125227250.jpg\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003e3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic\nParsing of Large-scale 3D Point Clouds (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1707.06783.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"40%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/3DCNN-DQN-RNN.png\" /\u003e\u003c/p\u003e\n\n:game_die::space_invader: \u003cb\u003eSemantic Segmentation of Indoor Point Clouds using Convolutional Neural Networks (2017)\u003c/b\u003e [[Paper]](https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-4-W4/101/2017/isprs-annals-IV-4-W4-101-2017.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"55%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Semantic Segmentation of Indoor Point Clouds using Convolutional Neural Networks.png\" /\u003e\u003c/p\u003e\n\n:game_die::space_invader: \u003cb\u003eSEGCloud: Semantic Segmentation of 3D Point Clouds (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1710.07563.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"55%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/SEGCloud.png\" /\u003e\u003c/p\u003e\n\n:game_die::space_invader: \u003cb\u003eLarge-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55 (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1710.06104.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"40%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Core55.png\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003ePointwise Convolutional Neural Networks (CVPR 2018)\u003c/b\u003e [[Link]](http://pointwise.scenenn.net/)\n\u003cbr\u003e\nWe propose pointwise convolution that performs on-the-fly voxelization for learning local features of a point cloud.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://pointwise.scenenn.net/images/teaser.png\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003eDynamic Graph CNN for Learning on Point Clouds (2018)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1801.07829.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://liuziwei7.github.io/homepage_files/dynamicgcnn_logo.png\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003ePointCNN (2018)\u003c/b\u003e [[Paper]](https://yangyanli.github.io/PointCNN/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://yangyan.li/images/paper/pointcnn.png\" /\u003e\u003c/p\u003e\n\n:camera::space_invader: \u003cb\u003e3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation (2018)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1803.10409.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/angeladai/3DMV/blob/master/images/teaser.jpg\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans (2018)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1712.10215.pdf) \n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/angeladai/ScanComplete/blob/master/images/teaser_mesh.jpg\" /\u003e\u003c/p\u003e\n\n:game_die::camera: \u003cb\u003eSPLATNet: Sparse Lattice Networks for Point Cloud Processing (2018)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1802.08275.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/SPLATNet-%20Sparse%20Lattice%20Networks%20for%20Point%20Cloud%20Processing.jpeg\" /\u003e\u003c/p\u003e\n\n:game_die::space_invader: \u003cb\u003ePointGrid: A Deep Network for 3D Shape Understanding (CVPR 2018) \u003c/b\u003e [[Paper]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Le_PointGrid_A_Deep_CVPR_2018_paper.pdf) [[Code]](https://github.com/trucleduc/PointGrid)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/PointGrid-%20A%20Deep%20Network%20for%203D%20Shape%20Understanding%20(2018).jpeg\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003ePointConv (2018)\u003c/b\u003e [[Paper]](https://github.com/DylanWusee/pointconv/tree/master/imgs)[[Code]](https://github.com/DylanWusee/pointconv/tree/master/imgs)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://pics4.baidu.com/feed/8b82b9014a90f603272fe29f88ef061fb251ed49.jpeg?token=b23e1dbbaeaf12ffe3d168bd997a8d66\u0026s=01307D328FE07C010C69C1CE0000D0B3\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003eSpiderCNN (2018)\u003c/b\u003e [[Paper]](https://github.com/xyf513/SpiderCNN)[[Code]](https://github.com/xyf513/SpiderCNN)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://5b0988e595225.cdn.sohucs.com/images/20181109/45c3b670e67f43b288791c650fb7fb0b.jpeg\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003e3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans (CVPR 2019)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1812.07003.pdf)[[Code]](https://github.com/Sekunde/3D-SIS)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://www.niessnerlab.org/papers/2019/6sis/teaser.jpg\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003eReal-time Progressive 3D Semantic Segmentation for Indoor Scenes (WACV 2019)\u003c/b\u003e [[Link]](https://pqhieu.github.io/research/proseg/)\n\u003cbr\u003e\nWe propose an efficient yet robust technique for on-the-fly dense reconstruction and semantic segmentation of 3D indoor scenes. Our method is built atop an efficient super-voxel clustering method and a conditional random field with higher-order constraints from structural and object cues, enabling progressive dense semantic segmentation without any precomputation.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://pqhieu.github.io/media/images/wacv19/thumbnail.gif\" /\u003e\u003c/p\u003e\n\n\n:game_die: \u003cb\u003eJSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds (CVPR 2019)\u003c/b\u003e [[Link]](https://pqhieu.github.io/research/jsis3d/)\n\u003cbr\u003e\nWe jointly address the problems of semantic and instance segmentation of 3D point clouds with a multi-task pointwise network that simultaneously performs two tasks: predicting the semantic classes of 3D points and embedding the points into high-dimensional vectors so that points of the same object instance are represented by similar embeddings. We then propose a multi-value conditional random field model to incorporate the semantic and instance labels and formulate the problem of semantic and instance segmentation as jointly optimising labels in the field model.\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"./imgs/jsis3d.png\" /\u003e\u003c/p\u003e\n\n\n:game_die: \u003cb\u003eShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics (ICCV 2019)\u003c/b\u003e [[Link]](https://hkust-vgd.github.io/shellnet/)\n\u003cbr\u003e\nWe propose an efficient end-to-end permutation invariant convolution for point cloud deep learning. We use statistics from concentric spherical shells to define representative features and resolve the point order ambiguity, allowing traditional convolution to perform efficiently on such features. \n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://hkust-vgd.github.io/shellnet/images/shellconv_new.png\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003eRotation Invariant Convolutions for 3D Point Clouds Deep Learning (3DV 2019)\u003c/b\u003e [[Link]](https://hkust-vgd.github.io/riconv/)\n\u003cbr\u003e\nWe introduce a novel convolution operator for point clouds that achieves rotation invariance. Our core idea is to use low-level rotation invariant geometric features such as distances and angles to design a convolution operator for point cloud learning. \n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://hkust-vgd.github.io/riconv/images/RIO_cam.png\" /\u003e\u003c/p\u003e\n\n\n\u003ca name=\"3d_synthesis\" /\u003e\n\n## 3D Model Synthesis/Reconstruction\n\n\u003ca name=\"3d_synthesis_model_based\" /\u003e\n\n### Parametric Morphable Model-based methods\n\n\u003cb\u003eA Morphable Model For The Synthesis Of 3D Faces (1999)\u003c/b\u003e [[Paper]](http://gravis.dmi.unibas.ch/publications/Sigg99/morphmod2.pdf)[[Code]](https://github.com/MichaelMure/3DMM)\n\u003cp align=\"center\"\u003e\u003cimg width=\"40%\" src=\"http://mblogthumb3.phinf.naver.net/MjAxNzAzMTdfMjcz/MDAxNDg5NzE3MzU0ODI3.9lQioLxwoGmtoIVXX9sbVOzhezoqgKMKiTovBnbUFN0g.sXN5tG4Kohgk7OJEtPnux-mv7OAoXVxxCyo3SGZMc6Yg.PNG.atelierjpro/031717_0222_DataDrivenS4.png?type=w420\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eFLAME: Faces Learned with an Articulated Model and Expressions (2017)\u003c/b\u003e [[Paper]](https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/400/paper.pdf)[[Code (Chumpy)]](https://github.com/Rubikplayer/flame-fitting)[[Code (TF)]](https://github.com/TimoBolkart/TF_FLAME) [[Code (PyTorch)]](https://github.com/HavenFeng/photometric_optimization)\n\u003cbr\u003e[FLAME](http://flame.is.tue.mpg.de/) is a lightweight and expressive generic head model learned from over 33,000 of accurately aligned 3D scans. The model combines a linear identity shape space (trained from 3800 scans of human heads) with an articulated neck, jaw, and eyeballs, pose-dependent corrective blendshapes, and additional global expression blendshapes.\nThe code demonstrates how to 1) reconstruct textured 3D faces from images, 2) fit the model to 3D landmarks or registered 3D meshes, or 3) generate 3D face templates for [speech-driven facial animation](https://github.com/TimoBolkart/voca).\n\u003cp align=\"center\"\u003e \u003cimg width=\"50%\" src=\"https://github.com/TimoBolkart/TF_FLAME/blob/master/gifs/model_variations.gif\"\u003e\u003c/p\u003e\n\n\u003cb\u003eThe Space of Human Body Shapes: Reconstruction and Parameterization from Range Scans (2003)\u003c/b\u003e [[Paper]](http://grail.cs.washington.edu/projects/digital-human/pub/allen03space-submit.pdf)\n\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/46d39b0e21ae956e4bcb7a789f92be480d45ee12/7-Figure10-1.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eSMPL-X: Expressive Body Capture: 3D Hands, Face, and Body from a Single Image (2019)\u003c/b\u003e [[Paper]](https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/497/SMPL-X.pdf)[[Video]](https://youtu.be/XyXIEmapWkw)[[Code]](https://github.com/vchoutas/smplify-x)\n\n\u003cp align=\"center\"\u003e \u003cimg width=\"50%\" src=\"https://github.com/vchoutas/smplify-x/blob/master/images/teaser_fig.png\"\u003e\u003c/p\u003e\n\n\u003cb\u003ePIFuHD: Multi-Level Pixel Aligned Implicit Function for High-Resolution 3D Human Digitization (CVPR 2020)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/2004.00452.pdf)[[Video]](https://www.youtube.com/watch?v=uEDqCxvF5yc\u0026feature=youtu.be)[[Code]](https://github.com/facebookresearch/pifuhd)\n\u003cp align=\"center\"\u003e \u003cimg width=\"50%\" src=\"\"\u003e\u003c/p\u003e\n\n\n\n\u003cb\u003eExPose: Monocular Expressive Body Regression through Body-Driven Attention (2020)\u003c/b\u003e [[Paper]](https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/620/0983.pdf)[[Video]](https://youtu.be/lNTmHLYTiB8)[[Code]](https://github.com/vchoutas/expose)\n\u003cp align=\"center\"\u003e \u003cimg width=\"50%\" src=\"https://github.com/vchoutas/expose/blob/master/images/expose.png\"\u003e\u003c/p\u003e\n\n\u003cb\u003eCategory-Specific Object Reconstruction from a Single Image (2014)\u003c/b\u003e [[Paper]](https://people.eecs.berkeley.edu/~akar/categoryshapes.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://people.eecs.berkeley.edu/~akar/categoryShapes/images/teaser.png\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003eDeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image (2017)\u003c/b\u003e [[Paper]](http://ai.stanford.edu/~haosu/papers/SI2PC_arxiv_submit.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://chrischoy.github.io/images/publication/deformnet/model.png\" /\u003e\u003c/p\u003e\n\n:gem: \u003cb\u003eMesh-based Autoencoders for Localized Deformation Component Analysis (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1709.04304.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://qytan.com/img/point_conv.jpg\" /\u003e\u003c/p\u003e\n\n:gem: \u003cb\u003eExploring Generative 3D Shapes Using Autoencoder Networks (Autodesk 2017)\u003c/b\u003e [[Paper]](https://www.autodeskresearch.com/publications/exploring_generative_3d_shapes)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Exploring%20Generative%203D%20Shapes%20Using%20Autoencoder%20Networks.jpeg\" /\u003e\u003c/p\u003e\n\n:gem: \u003cb\u003eUsing Locally Corresponding CAD Models for\nDense 3D Reconstructions from a Single Image (2017)\u003c/b\u003e [[Paper]](http://ci2cv.net/media/papers/chenkong_cvpr_2017.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://chenhsuanlin.bitbucket.io/images/rp/r02.png\" /\u003e\u003c/p\u003e\n\n:gem: \u003cb\u003eCompact Model Representation for 3D Reconstruction (2017)\u003c/b\u003e [[Paper]](https://jhonykaesemodel.com/publication/3dv2017/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://jhonykaesemodel.com/img/headers/overview.png\" /\u003e\u003c/p\u003e\n\n:gem: \u003cb\u003eImage2Mesh: A Learning Framework for Single Image 3D Reconstruction (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1711.10669.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://pbs.twimg.com/media/DW5VhjpW4AAESHO.jpg\" /\u003e\u003c/p\u003e\n\n:gem: \u003cb\u003eLearning free-form deformations for 3D object reconstruction (2018)\u003c/b\u003e [[Paper]](https://jhonykaesemodel.com/publication/learning_ffd/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://jhonykaesemodel.com/learning_ffd_overview.png\" /\u003e\u003c/p\u003e\n\n:gem: \u003cb\u003eVariational Autoencoders for Deforming 3D Mesh Models(2018 CVPR)\u003c/b\u003e [[Paper]](http://qytan.com/publication/vae/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://humanmotion.ict.ac.cn/papers/2018P5_VariationalAutoencoders/TeaserImage.jpg\" /\u003e\u003c/p\u003e\n\n:gem: \u003cb\u003eLions and Tigers and Bears: Capturing Non-Rigid, 3D, Articulated Shape from Images (2018 CVPR)\u003c/b\u003e [[Paper]](http://files.is.tue.mpg.de/black/papers/zuffiCVPR2018.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://3c1703fe8d.site.internapcdn.net/newman/gfx/news/hires/2018/realisticava.jpg\" /\u003e\u003c/p\u003e\n\n\u003ca name=\"3d_synthesis_template_based\" /\u003e\n\n### Part-based Template Learning methods\n\n\u003cb\u003eModeling by Example (2004)\u003c/b\u003e [[Paper]](http://www.cs.princeton.edu/~funk/sig04a.pdf)\n\n\u003cp align=\"center\"\u003e\u003cimg width=\"20%\" src=\"http://gfx.cs.princeton.edu/pubs/Funkhouser_2004_MBE/chair.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eModel Composition from Interchangeable Components (2007)\u003c/b\u003e [[Paper]](http://www.cs.princeton.edu/courses/archive/spring11/cos598A/pdfs/Kraevoy07.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"40%\" src=\"http://www.cs.ubc.ca/labs/imager/tr/2007/Vlad_Shuffler/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eData-Driven Suggestions for Creativity Support in 3D Modeling (2010)\u003c/b\u003e [[Paper]](http://vladlen.info/publications/data-driven-suggestions-for-creativity-support-in-3d-modeling/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://vladlen.info/wp-content/uploads/2011/12/creativity.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003ePhoto-Inspired Model-Driven 3D Object Modeling (2011)\u003c/b\u003e [[Paper]](http://kevinkaixu.net/projects/photo-inspired.html)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://kevinkaixu.net/projects/photo-inspired/overview.PNG\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eProbabilistic Reasoning for Assembly-Based 3D Modeling (2011)\u003c/b\u003e [[Paper]](https://people.cs.umass.edu/~kalo/papers/assembly/ProbReasoningShapeModeling.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://vladlen.info/wp-content/uploads/2011/12/highlight9.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eA Probabilistic Model for Component-Based Shape Synthesis (2012)\u003c/b\u003e [[Paper]](https://people.cs.umass.edu/~kalo/papers/ShapeSynthesis/ShapeSynthesis.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/test1/blob/master/imgs/A%20Probabilistic%20Model%20for%20Component-Based%20Shape%20Synthesis.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eStructure Recovery by Part Assembly (2012)\u003c/b\u003e [[Paper]](http://cg.cs.tsinghua.edu.cn/StructureRecovery/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/test1/blob/master/imgs/Structure%20Recovery%20by%20Part%20Assembly.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eFit and Diverse: Set Evolution for Inspiring 3D Shape Galleries (2012)\u003c/b\u003e [[Paper]](http://kevinkaixu.net/projects/civil.html)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://kevinkaixu.net/projects/civil/teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eAttribIt: Content Creation with Semantic Attributes (2013)\u003c/b\u003e [[Paper]](https://people.cs.umass.edu/~kalo/papers/attribit/AttribIt.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"30%\" src=\"http://gfx.cs.princeton.edu/gfx/pubs/Chaudhuri_2013_ACC/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eLearning Part-based Templates from Large Collections of 3D Shapes (2013)\u003c/b\u003e [[Paper]](http://shape.cs.princeton.edu/vkcorrs/papers/13_SIGGRAPH_CorrsTmplt.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/test1/blob/master/imgs/Learning%20Part-based%20Templates%20from%20Large%20Collections%20of%203D%20Shapes.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eTopology-Varying 3D Shape Creation via Structural Blending (2014)\u003c/b\u003e [[Paper]](http://gruvi.cs.sfu.ca/project/topo/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://i.ytimg.com/vi/Xc4qf7v6a-w/maxresdefault.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eEstimating Image Depth using Shape Collections (2014)\u003c/b\u003e [[Paper]](http://vecg.cs.ucl.ac.uk/Projects/SmartGeometry/image_shape_net/imageShapeNet_sigg14.html)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://vecg.cs.ucl.ac.uk/Projects/SmartGeometry/image_shape_net/paper_docs/pipeline.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eSingle-View Reconstruction via Joint Analysis of Image and Shape Collections (2015)\u003c/b\u003e [[Paper]](https://www.cs.utexas.edu/~huangqx/modeling_sig15.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://vladlen.info/wp-content/uploads/2015/05/single-view.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eInterchangeable Components for Hands-On Assembly Based Modeling (2016)\u003c/b\u003e [[Paper]](http://www.cs.umb.edu/~craigyu/papers/handson_low_res.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"30%\" src=\"https://github.com/timzhang642/test1/blob/master/imgs/Interchangeable%20Components%20for%20Hands-On%20Assembly%20Based%20Modeling.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eShape Completion from a Single RGBD Image (2016)\u003c/b\u003e [[Paper]](http://www.kunzhou.net/2016/shapecompletion-tvcg16.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"40%\" src=\"http://tianjiashao.com/Images/2015/completion.jpg\" /\u003e\u003c/p\u003e\n\n\u003ca name=\"3d_synthesis_dl_based\" /\u003e\n\n### Deep Learning Methods\n\n:camera: \u003cb\u003eLearning to Generate Chairs, Tables and Cars with Convolutional Networks (2014)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1411.5928.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://zo7.github.io/img/2016-09-25-generating-faces/chairs-model.png\" /\u003e\u003c/p\u003e\n\n:camera: \u003cb\u003eWeakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis (2015, NIPS)\u003c/b\u003e [[Paper]](https://papers.nips.cc/paper/5639-weakly-supervised-disentangling-with-recurrent-transformations-for-3d-view-synthesis.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/jimeiyang/deepRotator/blob/master/demo_img.png\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003eAnalysis and synthesis of 3D shape families via deep-learned generative models of surfaces (2015)\u003c/b\u003e [[Paper]](https://people.cs.umass.edu/~hbhuang/publications/bsm/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://people.cs.umass.edu/~hbhuang/publications/bsm/bsm_teaser.jpg\" /\u003e\u003c/p\u003e\n\n:camera: \u003cb\u003eWeakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis (2015)\u003c/b\u003e [[Paper]](https://papers.nips.cc/paper/5639-weakly-supervised-disentangling-with-recurrent-transformations-for-3d-view-synthesis.pdf) [[Code]](https://github.com/jimeiyang/deepRotator)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/042993c46294a542946c9c1706b7b22deb1d7c43/2-Figure1-1.png\" /\u003e\u003c/p\u003e\n\n:camera: \u003cb\u003eMulti-view 3D Models from Single Images with a Convolutional Network (2016)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1511.06702.pdf) [[Code]](https://github.com/lmb-freiburg/mv3d)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/3d7ca5ad34f23a5fab16e73e287d1a059dc7ef9a/4-Figure2-1.png\" /\u003e\u003c/p\u003e\n\n:camera: \u003cb\u003eView Synthesis by Appearance Flow (2016)\u003c/b\u003e [[Paper]](https://people.eecs.berkeley.edu/~tinghuiz/papers/eccv16_appflow.pdf) [[Code]](https://github.com/tinghuiz/appearance-flow)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/12280506dc8b5c3ca2db29fc3be694d9a8bef48c/6-Figure2-1.png\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eVoxlets: Structured Prediction of Unobserved Voxels From a Single Depth Image (2016)\u003c/b\u003e [[Paper]](http://visual.cs.ucl.ac.uk/pubs/depthPrediction/http://visual.cs.ucl.ac.uk/pubs/depthPrediction/) [[Code]](https://github.com/mdfirman/voxlets)\n\u003cp align=\"center\"\u003e\u003cimg width=\"30%\" src=\"https://i.ytimg.com/vi/1wy4y2GWD5o/maxresdefault.jpg\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003e3D-R2N2: 3D Recurrent Reconstruction Neural Network (2016)\u003c/b\u003e [[Paper]](http://cvgl.stanford.edu/3d-r2n2/) [[Code]](https://github.com/chrischoy/3D-R2N2)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://3d-r2n2.stanford.edu/imgs/overview.png\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003ePerspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision (2016)\u003c/b\u003e [[Paper]](https://eng.ucmerced.edu/people/jyang44/papers/nips16_ptn.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"70%\" src=\"https://sites.google.com/site/skywalkeryxc/_/rsrc/1481104596238/perspective_transformer_nets/network_arch.png\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eTL-Embedding Network: Learning a Predictable and Generative Vector Representation for Objects (2016)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1603.08637.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://rohitgirdhar.github.io/GenerativePredictableVoxels/assets/webteaser.jpg\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003e3D GAN: Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling (2016)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1610.07584.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://3dgan.csail.mit.edu/images/model.jpg\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003e3D Shape Induction from 2D Views of Multiple Objects (2016)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1612.05872.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/e78572eeef8b967dec420013c65a6684487c13b2/2-Figure2-1.png\" /\u003e\u003c/p\u003e\n\n:camera: \u003cb\u003eUnsupervised Learning of 3D Structure from Images (2016)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1607.00662.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://adriancolyer.files.wordpress.com/2016/12/unsupervised-3d-fig-10.jpeg?w=600\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eGenerative and Discriminative Voxel Modeling with Convolutional Neural Networks (2016)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1608.04236.pdf) [[Code]](https://github.com/ajbrock/Generative-and-Discriminative-Voxel-Modeling)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://davidstutz.de/wordpress/wp-content/uploads/2017/02/brock_vae.png\" /\u003e\u003c/p\u003e\n\n:camera: \u003cb\u003eMulti-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency (2017)\u003c/b\u003e [[Paper]](https://shubhtuls.github.io/drc/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://shubhtuls.github.io/drc/resources/images/teaserChair.png\" /\u003e\u003c/p\u003e\n\n:camera: \u003cb\u003eSynthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes with Deep Generative Networks (2017)\u003c/b\u003e [[Paper]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Soltani_Synthesizing_3D_Shapes_CVPR_2017_paper.pdf)  [[Code]](https://github.com/Amir-Arsalan/Synthesize3DviaDepthOrSil)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://jiajunwu.com/images/spotlight_3dvae.jpg\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eShape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1612.00101.pdf) [[Code]](https://github.com/angeladai/cnncomplete)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://graphics.stanford.edu/projects/cnncomplete/teaser.jpg\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eOctree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1703.09438.pdf) [[Code]](https://github.com/lmb-freiburg/ogn)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/6c2a292bb018a8742cbb0bbc5e23dd0a454ffe3a/2-Figure2-1.png\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eHierarchical Surface Prediction for 3D Object Reconstruction (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1704.00710.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://bair.berkeley.edu/blog/assets/hsp/image_2.png\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eOctNetFusion: Learning Depth Fusion from Data (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1704.01047.pdf) [[Code]](https://github.com/griegler/octnetfusion)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/OctNetFusion-%20Learning%20Depth%20Fusion%20from%20Data.jpeg\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003eA Point Set Generation Network for 3D Object Reconstruction from a Single Image (2017)\u003c/b\u003e [[Paper]](http://ai.stanford.edu/~haosu/papers/SI2PC_arxiv_submit.pdf) [[Code]](https://github.com/fanhqme/PointSetGeneration)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/A%20Point%20Set%20Generation%20Network%20for%203D%20Object%20Reconstruction%20from%20a%20Single%20Image%20(2017).jpeg\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003eLearning Representations and Generative Models for 3D Point Clouds (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1707.02392.pdf) [[Code]](https://github.com/optas/latent_3d_points)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/optas/latent_3d_points/blob/master/doc/images/teaser.jpg\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003eShape Generation using Spatially Partitioned Point Clouds (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1707.06267.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://mgadelha.me/sppc/fig/abstract.png\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003ePCPNET Learning Local Shape Properties from Raw Point Clouds (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1710.04954.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/PCPNET%20Learning%20Local%20Shape%20Properties%20from%20Raw%20Point%20Clouds%20(2017).jpeg\" /\u003e\u003c/p\u003e\n\n:camera: \u003cb\u003eTransformation-Grounded Image Generation Network for Novel 3D View Synthesis (2017)\u003c/b\u003e [[Paper]](http://www.cs.unc.edu/~eunbyung/tvsn/) [[Code]](https://github.com/silverbottlep/tvsn)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://eng.ucmerced.edu/people/jyang44/pics/view_synthesis.gif\" /\u003e\u003c/p\u003e\n\n:camera: \u003cb\u003eTag Disentangled Generative Adversarial Networks for Object Image Re-rendering (2017)\u003c/b\u003e [[Paper]](http://static.ijcai.org/proceedings-2017/0404.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Tag%20Disentangled%20Generative%20Adversarial%20Networks%20for%20Object%20Image%20Re-rendering.jpeg\" /\u003e\u003c/p\u003e\n\n:camera: \u003cb\u003e3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks (2017)\u003c/b\u003e [[Paper]](http://people.cs.umass.edu/~zlun/papers/SketchModeling/) [[Code]](https://github.com/happylun/SketchModeling)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://people.cs.umass.edu/~zlun/papers/SketchModeling/SketchModeling_teaser.png\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eInteractive 3D Modeling with a Generative Adversarial Network (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1706.05170.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://pbs.twimg.com/media/DCsPKLqXoAEBd-V.jpg\" /\u003e\u003c/p\u003e\n\n:camera::space_invader: \u003cb\u003eWeakly supervised 3D Reconstruction with Adversarial Constraint (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1705.10904.pdf) [[Code]](https://github.com/jgwak/McRecon)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Weakly%20supervised%203D%20Reconstruction%20with%20Adversarial%20Constraint%20(2017).jpeg\" /\u003e\u003c/p\u003e\n\n:camera: \u003cb\u003eSurfNet: Generating 3D shape surfaces using deep residual networks (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1703.04079.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://3dadept.com/wp-content/uploads/2017/07/Screenshot-from-2017-07-26-145521-e1501077539723.png\" /\u003e\u003c/p\u003e\n\n:camera: \u003cb\u003eLearning to Reconstruct Symmetric Shapes using Planar Parameterization of 3D Surface (2019)\u003c/b\u003e [[Paper]](https://openaccess.thecvf.com/content_ICCVW_2019/papers/GMDL/Jain_Learning_to_Reconstruct_Symmetric_Shapes_using_Planar_Parameterization_of_3D_ICCVW_2019_paper.pdf) [[Code]](https://github.com/hrdkjain/LearningSymmetricShapes)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/hrdkjain/LearningSymmetricShapes/blob/master/Images/teaser.png\" /\u003e\u003c/p\u003e\n\n:pill: \u003cb\u003eGRASS: Generative Recursive Autoencoders for Shape Structures (SIGGRAPH 2017)\u003c/b\u003e [[Paper]](http://kevinkaixu.net/projects/grass.html) [[Code]](https://github.com/junli-lj/grass) [[code]](https://github.com/kevin-kaixu/grass_pytorch)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://kevinkaixu.net/projects/grass/teaser.jpg\" /\u003e\u003c/p\u003e\n\n:pill: \u003cb\u003e 3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1708.01648.pdf)[[code]](https://github.com/zouchuhang/3D-PRNN)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/zouchuhang/3D-PRNN/blob/master/figs/teasor.jpg\" /\u003e\u003c/p\u003e\n\n:gem: \u003cb\u003eNeural 3D Mesh Renderer (2017)\u003c/b\u003e [[Paper]](http://hiroharu-kato.com/projects_en/neural_renderer.html) [[Code]](https://github.com/hiroharu-kato/neural_renderer.git)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://pbs.twimg.com/media/DPSm-4HWkAApEZd.jpg\" /\u003e\u003c/p\u003e\n\n:game_die::space_invader: \u003cb\u003eLarge-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55 (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1710.06104.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"40%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Core55.png\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003ePix2vox: Sketch-Based 3D Exploration with Stacked Generative Adversarial Networks (2017)\u003c/b\u003e [[Code]](https://github.com/maxorange/pix2vox)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/maxorange/pix2vox/blob/master/img/sample.gif\" /\u003e\u003c/p\u003e\n\n:camera::space_invader: \u003cb\u003eWhat You Sketch Is What You Get: 3D Sketching using Multi-View Deep Volumetric Prediction (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1707.08390.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://arxiv-sanity-sanity-production.s3.amazonaws.com/render-output/31631/x1.png\" /\u003e\u003c/p\u003e\n\n:camera::space_invader: \u003cb\u003eMarrNet: 3D Shape Reconstruction via 2.5D Sketches (2017)\u003c/b\u003e [[Paper]](http://marrnet.csail.mit.edu/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://marrnet.csail.mit.edu/images/model.jpg\" /\u003e\u003c/p\u003e\n\n:camera::space_invader::game_die: \u003cb\u003eLearning a Multi-View Stereo Machine (2017 NIPS)\u003c/b\u003e [[Paper]](http://bair.berkeley.edu/blog/2017/09/05/unified-3d/) \n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://bair.berkeley.edu/static/blog/unified-3d/Network.png\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003e3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions (2017)\u003c/b\u003e [[Paper]](http://3dmatch.cs.princeton.edu/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://3dmatch.cs.princeton.edu/img/overview.jpg\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eScaling CNNs for High Resolution Volumetric Reconstruction from a Single Image (2017)\u003c/b\u003e [[Paper]](https://ieeexplore.ieee.org/document/8265323/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/frankhjwx/3D-Machine-Learning/blob/master/imgs/Scaling%20CNN%20Reconstruction.png\" /\u003e\u003c/p\u003e\n\n:pill: \u003cb\u003eComplementMe: Weakly-Supervised Component Suggestions for 3D Modeling (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1708.01841.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://mhsung.github.io/assets/images/complement-me/figure_2.png\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eLearning Descriptor Networks for 3D Shape Synthesis and Analysis (2018 CVPR)\u003c/b\u003e    [[Project]](http://www.stat.ucla.edu/~jxie/3DEBM/) [[Paper]](http://www.stat.ucla.edu/~jxie/3DDescriptorNet/3DDescriptorNet_file/doc/3DDescriptorNet.pdf) [[Code](https://github.com/jianwen-xie/3DDescriptorNet)]\n\nAn energy-based 3D shape descriptor network is a deep energy-based model for volumetric shape patterns. The maximum likelihood training of the model follows an “analysis by synthesis” scheme and can be interpreted as a mode seeking and mode shifting process. The model can synthesize 3D shape patterns by sampling from the probability distribution via MCMC such as Langevin dynamics. Experiments demonstrate that the proposed model can generate realistic 3D shape patterns and can be useful for 3D shape analysis.\n\n\u003cp align=\"center\"\u003e\u003cimg width=\"60%\" src=\"http://www.stat.ucla.edu/~jxie/3DEBM/files/3D_syn.png\" /\u003e\u003c/p\u003e \n\n:game_die: \u003cb\u003ePU-Net: Point Cloud Upsampling Network (2018)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1801.06761.pdf) [[Code]](https://github.com/yulequan/PU-Net)\n\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://appsrv.cse.cuhk.edu.hk/~lqyu/indexpics/Pu-Net.png\" /\u003e\u003c/p\u003e \n\n:camera::space_invader: \u003cb\u003eMulti-view Consistency as Supervisory Signal  for Learning Shape and Pose Prediction (2018 CVPR)\u003c/b\u003e [[Paper]](https://shubhtuls.github.io/mvcSnP/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://shubhtuls.github.io/mvcSnP/resources/images/teaser.png\" /\u003e\u003c/p\u003e\n\n:camera::game_die: \u003cb\u003eObject-Centric Photometric Bundle Adjustment with Deep Shape Prior (2018)\u003c/b\u003e [[Paper]](http://ci2cv.net/media/papers/WACV18.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://chenhsuanlin.bitbucket.io/images/rp/r06.png\" /\u003e\u003c/p\u003e\n\n:camera::game_die: \u003cb\u003eLearning Efficient Point Cloud Generation for Dense 3D Object Reconstruction (2018 AAAI)\u003c/b\u003e [[Paper]](https://chenhsuanlin.bitbucket.io/3D-point-cloud-generation/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://chenhsuanlin.bitbucket.io/images/rp/r05.png\" /\u003e\u003c/p\u003e\n\n:gem: \u003cb\u003ePixel2Mesh: Generating 3D Mesh Models from Single RGB Images (2018)\u003c/b\u003e [[Paper]](https://github.com/nywang16/Pixel2Mesh)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://www.groundai.com/media/arxiv_projects/188911/x2.png.750x0_q75_crop.png\" /\u003e\u003c/p\u003e\n\n:gem: \u003cb\u003eAtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation (2018 CVPR)\u003c/b\u003e [[Paper]](http://imagine.enpc.fr/~groueixt/atlasnet/) [[Code]](https://github.com/ThibaultGROUEIX/AtlasNet)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://imagine.enpc.fr/~groueixt/atlasnet/imgs/teaser.small.png\" /\u003e\u003c/p\u003e\n\n:space_invader::gem: \u003cb\u003eDeep Marching Cubes: Learning Explicit Surface Representations (2018 CVPR)\u003c/b\u003e [[Paper]](http://www.cvlibs.net/publications/Liao2018CVPR.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/frankhjwx/3D-Machine-Learning/blob/master/imgs/Deep%20Marching%20Cubes.png\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eIm2Avatar: Colorful 3D Reconstruction from a Single Image (2018)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1804.06375v1.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/syb7573330/im2avatar/blob/master/misc/demo_teaser.png\" /\u003e\u003c/p\u003e\n\n:gem: \u003cb\u003eLearning Category-Specific Mesh Reconstruction  from Image Collections (2018)\u003c/b\u003e [[Paper]](https://akanazawa.github.io/cmr/#)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://akanazawa.github.io/cmr/resources/images/teaser.png\" /\u003e\u003c/p\u003e\n\n:pill: \u003cb\u003eCSGNet: Neural Shape Parser for Constructive Solid Geometry (2018)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1712.08290.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://pbs.twimg.com/media/DR-RgbaU8AEyjeW.jpg\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eText2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings (2018)\u003c/b\u003e [[Paper]](http://text2shape.stanford.edu/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://text2shape.stanford.edu/figures/pull.png\" /\u003e\u003c/p\u003e\n\n:space_invader::gem::camera: \u003cb\u003eMulti-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation (2018)\u003c/b\u003e  [[Paper]](https://arxiv.org/abs/1802.09987) [[Code]](https://github.com/EdwardSmith1884/Multi-View-Silhouette-and-Depth-Decomposition-for-High-Resolution-3D-Object-Representation)\n\u003cp align=\"center\"\u003e\u003cimg width=\"60%\" src=\"imgs/decomposition_new.png\" /\u003e \u003cimg width=\"60%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Multi-View%20Silhouette%20and%20Depth%20Decomposition%20for%20High%20Resolution%203D%20Object%20Representation.png\" /\u003e\u003c/p\u003e\n\n:space_invader::gem::camera: \u003cb\u003ePixels, voxels, and views: A study of shape representations for single view 3D object shape prediction (2018 CVPR)\u003c/b\u003e  [[Paper]](https://arxiv.org/abs/1804.06032)\n\u003cp align=\"center\"\u003e\u003cimg width=\"60%\" src=\"imgs/pixels-voxels-views-rgb2mesh.png\" /\u003e \u003c/p\u003e\n\n:camera::game_die: \u003cb\u003eNeural scene representation and rendering (2018)\u003c/b\u003e [[Paper]](https://deepmind.com/blog/neural-scene-representation-and-rendering/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://www.arimorcos.com/static/images/publication_images/gqn_image.png\" /\u003e\u003c/p\u003e\n\n:pill: \u003cb\u003eIm2Struct: Recovering 3D Shape Structure from a Single RGB Image (2018 CVPR)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1804.05469.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://kevinkaixu.net/images/publications/niu_cvpr18.jpg\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003eFoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation (2018 CVPR)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1712.07262.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://simbaforrest.github.io/fig/FoldingNet.jpg\" /\u003e\u003c/p\u003e\n\n:camera::space_invader: \u003cb\u003ePix3D: Dataset and Methods for Single-Image 3D Shape Modeling (2018 CVPR)\u003c/b\u003e [[Paper]](http://pix3d.csail.mit.edu/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Pix3D%20-%20Dataset%20and%20Methods%20for%20Single-Image%203D%20Shape%20Modeling%20(2018%20CVPR).png\" /\u003e\u003c/p\u003e\n\n:gem: \u003cb\u003e3D-RCNN: Instance-level 3D Object Reconstruction via Render-and-Compare (2018 CVPR)\u003c/b\u003e [[Paper]](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/1128.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/3D-RCNN-%20Instance-level%203D%20Object%20Reconstruction%20via%20Render-and-Compare%20(2018%20CVPR).jpeg\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eMatryoshka Networks: Predicting 3D Geometry via Nested Shape Layers (2018 CVPR)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1804.10975.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Matryoshka%20Networks-%20Predicting%203D%20Geometry%20via%20Nested%20Shape%20Layers%20(2018%20CVPR).jpeg\" /\u003e\u003c/p\u003e\n\n:gem: \u003cb\u003e\t\nDeformable Shape Completion with Graph Convolutional Autoencoders (2018 CVPR)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1712.00268v1.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://orlitany.github.io/OL_files/shapeComp.png\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eGlobal-to-Local Generative Model for 3D Shapes (SIGGRAPH Asia 2018)\u003c/b\u003e [[Paper]](http://vcc.szu.edu.cn/research/2018/G2L)[[Code]](https://github.com/Hao-HUST/G2LGAN)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Global-to-Local%20Generative%20Model%20for%203D%20Shapes.jpg\" /\u003e\u003c/p\u003e\n\n:gem::game_die::space_invader: \u003cb\u003eALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learning (TOG 2018)\u003c/b\u003e [[Paper]](https://bit.ly/alignet) [[Code]](https://github.com/ranahanocka/ALIGNet/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/ranahanocka/ALIGNet/blob/master/docs/rep.png\" /\u003e\u003c/p\u003e\n\n:game_die::space_invader: \u003cb\u003ePointGrid: A Deep Network for 3D Shape Understanding (CVPR 2018) \u003c/b\u003e [[Paper]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Le_PointGrid_A_Deep_CVPR_2018_paper.pdf) [[Code]](https://github.com/trucleduc/PointGrid)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/PointGrid-%20A%20Deep%20Network%20for%203D%20Shape%20Understanding%20(2018).jpeg\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003eGAL: Geometric Adversarial Loss for Single-View 3D-Object Reconstruction (2018)\u003c/b\u003e [[Paper]](https://xjqi.github.io/GAL.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://media.springernature.com/original/springer-static/image/chp%3A10.1007%2F978-3-030-01237-3_49/MediaObjects/474213_1_En_49_Fig2_HTML.gif\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003eVisual Object Networks: Image Generation with Disentangled 3D Representation (2018)\u003c/b\u003e [[Paper]](https://papers.nips.cc/paper/7297-visual-object-networks-image-generation-with-disentangled-3d-representations.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Visual%20Object%20Networks-%20Image%20Generation%20with%20Disentangled%203D%20Representation%20(2018).jpeg\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eLearning to Infer and Execute 3D Shape Programs (2019))\u003c/b\u003e [[Paper]](http://shape2prog.csail.mit.edu/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://shape2prog.csail.mit.edu/shape_files/teaser.jpg\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eLearning to Infer and Execute 3D Shape Programs (2019))\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1901.05103.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://pbs.twimg.com/media/DxFaW-mU8AEo9wc.jpg\" /\u003e\u003c/p\u003e\n\n:gem: \u003cb\u003eLearning View Priors for Single-view 3D Reconstruction (CVPR 2019)\u003c/b\u003e [[Paper]](http://hiroharu-kato.com/projects_en/view_prior_learning.html)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Learning%20View%20Priors%20for%20Single-view%203D%20Reconstruction.png\" /\u003e\u003c/p\u003e\n\n:gem::game_die: \u003cb\u003eLearning Embedding of 3D models with Quadric Loss (BMVC 2019)\u003c/b\u003e [[Paper]](https://arxiv.org/abs/1907.10250) [[Code]](https://github.com/nitinagarwal/QuadricLoss)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://www.ics.uci.edu/~agarwal/bmvc_2019.png\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003eCompoNet: Learning to Generate the Unseen by Part Synthesis and Composition (ICCV 2019)\u003c/b\u003e [[Paper]](https://arxiv.org/abs/1811.07441)[[Code]](https://github.com/nschor/CompoNet)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://raw.githubusercontent.com/nschor/CompoNet/master/images/network_architecture.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eCoMA: Convolutional Mesh Autoencoders (2018)\u003c/b\u003e [[Paper]](https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/439/1285.pdf)[[Code (TF)]](https://github.com/anuragranj/coma)[[Code (PyTorch)]](https://github.com/pixelite1201/pytorch_coma/)[[Code (PyTorch)]](https://github.com/sw-gong/coma)\n\u003cbr\u003e[CoMA](https://coma.is.tue.mpg.de/) is a versatile model that learns a non-linear representation of a face using spectral convolutions on a mesh surface. CoMA introduces mesh sampling operations that enable a hierarchical mesh representation that captures non-linear variations in shape and expression at multiple scales within the model. \n\u003cp align=\"center\"\u003e \u003cimg width=\"50%\" src=\"https://coma.is.tue.mpg.de/uploads/ckeditor/pictures/91/content_coma_faces.jpg\"\u003e\u003c/p\u003e\n\n\u003cb\u003eRingNet: 3D Face Reconstruction from Single Images (2019)\u003c/b\u003e [[Paper]](https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/509/paper_camera_ready.pdf)[[Code]](https://github.com/soubhiksanyal/RingNet)\n\u003cp align=\"center\"\u003e \u003cimg width=\"50%\" src=\"https://github.com/soubhiksanyal/RingNet/blob/master/gif/celeba_reconstruction.gif\"\u003e\u003c/p\u003e\n\n\u003cb\u003eVOCA: Voice Operated Character Animation (2019)\u003c/b\u003e [[Paper]](https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/510/paper_final.pdf)[[Video]](https://youtu.be/XceCxf_GyW4)[[Code]](https://github.com/TimoBolkart/voca)\n\u003cbr\u003e[VOCA](https://voca.is.tue.mpg.de/) is a simple and generic speech-driven facial animation framework that works across a range of identities. The codebase demonstrates how to synthesize realistic character animations given an arbitrary speech signal and a static character mesh.\n\u003cp align=\"center\"\u003e \u003cimg width=\"50%\" src=\"https://github.com/TimoBolkart/voca/blob/master/gif/speech_driven_animation.gif\"\u003e\u003c/p\u003e\n\n:gem: \u003cb\u003eLearning to Predict 3D Objects with an Interpolation-based Differentiable Renderer\u003c/b\u003e [[Paper]](https://arxiv.org/abs/1908.01210)[[Site]](https://nv-tlabs.github.io/DIB-R/)[[Code]](https://github.com/nv-tlabs/DIB-R)\n\u003cp align=\"center\"\u003e \u003cimg width=\"50%\" src=\"https://nv-tlabs.github.io/DIB-R/figures/model2a-2.png\"\u003e \u003c/p\u003e\n\n:gem: \u003cb\u003eSoft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning\u003c/b\u003e [[Paper]](https://arxiv.org/abs/1904.01786)[[Code]](https://github.com/ShichenLiu/SoftRas)\n\u003cp align=\"center\"\u003e \u003cimg width=\"50%\" src=\"https://raw.githubusercontent.com/ShichenLiu/SoftRas/master/data/media/teaser/teaser.png\"\u003e \u003c/p\u003e\n\n\u003cb\u003eNeRF: Representing Scenes as Neural Radiance Fields for View Synthesis\u003c/b\u003e [[Project]](http://www.matthewtancik.com/nerf)[[Paper]](https://arxiv.org/abs/2003.08934)[[Code]](https://github.com/bmild/nerf)\n\u003cp align=\"center\"\u003e \u003cimg width=\"50%\" src=\"https://uploads-ssl.webflow.com/51e0d73d83d06baa7a00000f/5e700ef6067b43821ed52768_pipeline_website-01-p-800.png\"\u003e \u003c/p\u003e\n\n:gem::game_die: \u003cb\u003eGAMesh: Guided and Augmented Meshing for Deep Point Networks (3DV 2020)\u003c/b\u003e [[Project]](https://www.ics.uci.edu/~agarwal/GAMesh/) [[Paper]](https://arxiv.org/abs/2010.09774) [[Code]](https://github.com/nitinagarwal/GAMesh)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://www.ics.uci.edu/~agarwal/3DV_2020.png\" /\u003e\u003c/p\u003e\n\n\n\n:space_invader: \u003cb\u003eGenerative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis (2020 TPAMI)\u003c/b\u003e   [[Paper]](http://www.stat.ucla.edu/~jxie/3DEBM/3DEBM_file/doc/gVoxelNet.pdf) \n\nThis paper proposes a deep 3D energy-based model to represent volumetric shapes. The maximum likelihood training of the model follows an “analysis by synthesis” scheme. Experiments demonstrate that the proposed model can generate high-quality 3D shape patterns and can be useful for a wide variety of 3D shape analysis.\n\n\u003cp align=\"center\"\u003e\u003cimg width=\"60%\" src=\"imgs/voxelnet.png\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003eGenerative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification (2021 CVPR) \u003c/b\u003e [[Project]](http://www.stat.ucla.edu/~jxie/GPointNet/) [[Paper]](https://arxiv.org/pdf/2004.01301.pdf) [[Code](https://github.com/fei960922/GPointNet)]\n\nGenerative PointNet is an energy-based model of unordered point clouds, where the energy function is parameterized by an input-permutation-invariant bottom-up neural network. The model can be trained by MCMC-based maximum likelihood learning, or a short-run MCMC toward the energy-based model as a flow-like generator for point cloud reconstruction and interpolation. The learned point cloud representation can be useful for point cloud classification. \n\n\u003cp align=\"center\"\u003e\u003cimg width=\"60%\" src=\"imgs/gpointnet.png\" /\u003e\u003c/p\u003e\n\n:game_die: :gem: \u003cb\u003eShape My Face: Registering 3D Face Scans by Surface-to-Surface Translation\u003c/b\u003e [[Paper]](https://arxiv.org/abs/2012.09235) [[Code]](https://github.com/mbahri/smf)\n\nShape My Face (SMF) is a point cloud to mesh auto-encoder for the registration of raw human face scans, and the generation of synthetic human faces. SMF leverages a modified PointNet encoder with a visual attention module and differentiable surface sampling to be independent of the original surface representation and reduce the need for pre-processing. Mesh convolution decoders are combined with a specialized PCA model of the mouth, and smoothly blended based on geodesic distances, to create a compact model that is highly robust to noise. SMF is applied to register and perform expression transfer on scans captured in-the-wild with an iPhone depth camera represented either as meshes or point clouds.\n\n\u003cp align=\"center\"\u003e\u003cimg width=\"60%\" src=\"imgs/ShapeMyFace.png\" /\u003e\u003c/p\u003e\n\n:game_die: \u003cb\u003eLearning Implicit Fields for Generative Shape Modeling (2019)\u003c/b\u003e [[Paper]](https://arxiv.org/abs/1812.02822) [[Code]](https://github.com/timzhang642/3D-Machine-Learning)\n\nWe advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes. An implicit field assigns a value to each point in 3D space, so that a shape can be extracted as an iso-surface. IM-NET is trained to perform this assignment by means of a binary classifier. Specifically, it takes a point coordinate, along with a feature vector encoding a shape, and outputs a value which indicates whether the point is outside the shape or not. By replacing conventional decoders by our implicit decoder for representation learning (via IM-AE) and shape generation (via IM-GAN), we demonstrate superior results for tasks such as generative shape modeling, interpolation, and single-view 3D reconstruction, particularly in terms of visual quality.\n\n\u003cp align=\"center\"\u003e\u003cimg width=\"60%\" src=\"imgs/IM_NET.png\" /\u003e\u003c/p\u003e\n\n\n\u003ca name=\"material_synthesis\" /\u003e\n\n## Texture/Material Analysis and Synthesis\n\u003cb\u003eTexture Synthesis Using Convolutional Neural Networks (2015)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1505.07376.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Texture%20Synthesis%20Using%20Convolutional%20Neural%20Networks.jpeg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eTwo-Shot SVBRDF Capture for Stationary Materials (SIGGRAPH 2015)\u003c/b\u003e [[Paper]](https://mediatech.aalto.fi/publications/graphics/TwoShotSVBRDF/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://mediatech.aalto.fi/publications/graphics/TwoShotSVBRDF/teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eReflectance Modeling by Neural Texture Synthesis (2016)\u003c/b\u003e [[Paper]](https://mediatech.aalto.fi/publications/graphics/NeuralSVBRDF/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://mediatech.aalto.fi/publications/graphics/NeuralSVBRDF/teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eModeling Surface Appearance from a Single Photograph using Self-augmented Convolutional Neural Networks (2017)\u003c/b\u003e [[Paper]](http://msraig.info/~sanet/sanet.htm)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://msraig.info/~sanet/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eHigh-Resolution Multi-Scale Neural Texture Synthesis (2017)\u003c/b\u003e [[Paper]](https://wxs.ca/research/multiscale-neural-synthesis/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://wxs.ca/research/multiscale-neural-synthesis/multiscale-gram-marble.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eReflectance and Natural Illumination from Single Material Specular Objects Using Deep Learning (2017)\u003c/b\u003e [[Paper]](https://homes.cs.washington.edu/~krematas/Publications/reflectance-natural-illumination.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://www.vision.ee.ethz.ch/~georgous/images/tpami17_teaser2.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eJoint Material and Illumination Estimation from Photo Sets in the Wild (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1710.08313.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Joint%20Material%20and%20Illumination%20Estimation%20from%20Photo%20Sets%20in%20the%20Wild.jpeg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eJWhat Is Around The Camera? (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1611.09325v2.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://homes.cs.washington.edu/~krematas/my_images/arxiv16b_teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eTextureGAN: Controlling Deep Image Synthesis with Texture Patches (2018 CVPR)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1706.02823.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://texturegan.eye.gatech.edu/img/paper_figure.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eGaussian Material Synthesis (2018 SIGGRAPH)\u003c/b\u003e [[Paper]](https://users.cg.tuwien.ac.at/zsolnai/gfx/gaussian-material-synthesis/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://i.ytimg.com/vi/VM2ysCnD9GA/maxresdefault.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eNon-stationary Texture Synthesis by Adversarial Expansion (2018 SIGGRAPH)\u003c/b\u003e [[Paper]](http://vcc.szu.edu.cn/research/2018/TexSyn)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/jessemelpolio/non-stationary_texture_syn/blob/master/imgs/teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eSynthesized Texture Quality Assessment via Multi-scale Spatial and Statistical Texture Attributes of Image and Gradient Magnitude Coefficients (2018 CVPR)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1804.08020.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://user-images.githubusercontent.com/12434910/39275366-e18c7c1c-4899-11e8-8e61-05072618bbce.PNG\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eLIME: Live Intrinsic Material Estimation (2018 CVPR)\u003c/b\u003e [[Paper]](https://gvv.mpi-inf.mpg.de/projects/LIME/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://web.stanford.edu/~zollhoef/papers/CVPR18_Material/teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eSingle-Image SVBRDF Capture with a Rendering-Aware Deep Network (2018)\u003c/b\u003e [[Paper]](https://team.inria.fr/graphdeco/fr/projects/deep-materials/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://team.inria.fr/graphdeco/files/2018/08/teaser_v0.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003ePhotoShape: Photorealistic Materials for Large-Scale Shape Collections (2018)\u003c/b\u003e [[Paper]](https://keunhong.com/publications/photoshape/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://keunhong.com/publications/photoshape/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eLearning Material-Aware Local Descriptors for 3D Shapes (2018)\u003c/b\u003e [[Paper]](http://www.vovakim.com/papers/18_3DV_ShapeMatFeat.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Learning%20Material-Aware%20Local%20Descriptors%20for%203D%20Shapes%20(2018).jpeg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eFrankenGAN: Guided Detail Synthesis for Building Mass Models \nusing Style-Synchonized GANs (2018 SIGGRAPH Asia)\u003c/b\u003e [[Paper]](http://geometry.cs.ucl.ac.uk/projects/2018/frankengan/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://geometry.cs.ucl.ac.uk/projects/2018/frankengan/paper_docs/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003ca name=\"style_transfer\" /\u003e\n\n## Style Learning and Transfer\n\u003cb\u003eStyle-Content Separation by Anisotropic Part Scales (2010)\u003c/b\u003e [[Paper]](https://www.cs.sfu.ca/~haoz/pubs/xu_siga10_style.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://sites.google.com/site/kevinkaixu/_/rsrc/1472852123106/publications/style_b.jpg?height=145\u0026width=400\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eDesign Preserving Garment Transfer (2012)\u003c/b\u003e [[Paper]](https://hal.inria.fr/hal-00695903/file/GarmentTransfer.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"30%\" src=\"https://hal.inria.fr/hal-00695903v2/file/02_WomanToAll.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eAnalogy-Driven 3D Style Transfer (2014)\u003c/b\u003e [[Paper]](http://www.chongyangma.com/publications/st/index.html)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://www.chongyangma.com/publications/st/2014_st_teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eElements of Style: Learning Perceptual Shape Style Similarity (2015)\u003c/b\u003e [[Paper]](http://people.cs.umass.edu/~zlun/papers/StyleSimilarity/StyleSimilarity.pdf) [[Code]](https://github.com/happylun/StyleSimilarity)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://people.cs.umass.edu/~zlun/papers/StyleSimilarity/StyleSimilarity_teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eFunctionality Preserving Shape Style Transfer (2016)\u003c/b\u003e [[Paper]](http://people.cs.umass.edu/~zlun/papers/StyleTransfer/StyleTransfer.pdf) [[Code]](https://github.com/happylun/StyleTransfer)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://people.cs.umass.edu/~zlun/papers/StyleTransfer/StyleTransfer_teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eUnsupervised Texture Transfer from Images to Model Collections (2016)\u003c/b\u003e [[Paper]](http://ai.stanford.edu/~haosu/papers/siga16_texture_transfer_small.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://geometry.cs.ucl.ac.uk/projects/2016/texture_transfer/paper_docs/teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eLearning Detail Transfer based on Geometric Features (2017)\u003c/b\u003e [[Paper]](http://surfacedetails.cs.princeton.edu/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://surfacedetails.cs.princeton.edu/images/teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eCo-Locating Style-Defining Elements on 3D Shapes (2017)\u003c/b\u003e [[Paper]](http://people.scs.carleton.ca/~olivervankaick/pubs/style_elem.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://s2017.siggraph.org/sites/default/files/styles/large/public/images/events/c118-e100-publicimage_0-itok=yO8OegQO.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eNeural 3D Mesh Renderer (2017)\u003c/b\u003e [[Paper]](http://hiroharu-kato.com/projects_en/neural_renderer.html) [[Code]](https://github.com/hiroharu-kato/neural_renderer.git)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://pbs.twimg.com/media/DPSm-4HWkAApEZd.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eAppearance Modeling via Proxy-to-Image Alignment (2018)\u003c/b\u003e [[Paper]](http://vcc.szu.edu.cn/research/2018/AppMod)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Appearance%20Modeling%20via%20Proxy-to-Image%20Alignment.png\" /\u003e\u003c/p\u003e\n\n:gem: \u003cb\u003ePixel2Mesh: Generating 3D Mesh Models from Single RGB Images (2018)\u003c/b\u003e [[Paper]](http://bigvid.fudan.edu.cn/pixel2mesh/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://pbs.twimg.com/media/DaIuEnfU0AAqesA.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eAutomatic Unpaired Shape Deformation Transfer (SIGGRAPH Asia 2018)\u003c/b\u003e [[Paper]](http://geometrylearning.com/ausdt/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://geometrylearning.com/ausdt/imgs/teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003e3DSNet: Unsupervised Shape-to-Shape 3D Style Transfer (2020)\u003c/b\u003e [[Paper]](https://arxiv.org/abs/2011.13388) [[Code]](https://github.com/ethz-asl/3dsnet)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/ethz-asl/3dsnet/blob/main/docs/chairs.jpg\" /\u003e\u003c/p\u003e\n\n\u003ca name=\"scene_synthesis\" /\u003e\n\n## Scene Synthesis/Reconstruction\n\u003cb\u003eMake It Home: Automatic Optimization of Furniture Arrangement (2011, SIGGRAPH)\u003c/b\u003e [[Paper]](http://people.sutd.edu.sg/~saikit/projects/furniture/index.html)\n\u003cp align=\"center\"\u003e\u003cimg width=\"40%\" src=\"https://www.cs.umb.edu/~craigyu/img/papers/furniture.gif\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eInteractive Furniture Layout Using Interior Design Guidelines (2011)\u003c/b\u003e [[Paper]](http://graphics.stanford.edu/~pmerrell/furnitureLayout.htm)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://vis.berkeley.edu/papers/furnitureLayout/furnitureBig.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eSynthesizing Open Worlds with Constraints using Locally Annealed Reversible Jump MCMC (2012)\u003c/b\u003e [[Paper]](http://graphics.stanford.edu/~lfyg/owl.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Synthesizing%20Open%20Worlds%20with%20Constraints%20using%20Locally%20Annealed%20Reversible%20Jump%20MCMC%20(2012).jpeg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eExample-based Synthesis of 3D Object Arrangements (2012 SIGGRAPH Asia)\u003c/b\u003e [[Paper]](http://graphics.stanford.edu/projects/scenesynth/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"60%\" src=\"http://graphics.stanford.edu/projects/scenesynth/img/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eSketch2Scene: Sketch-based Co-retrieval  and Co-placement of 3D Models  (2013)\u003c/b\u003e [[Paper]](http://sweb.cityu.edu.hk/hongbofu/projects/sketch2scene_sig13/#.WWWge__ysb0)\n\u003cp align=\"center\"\u003e\u003cimg width=\"40%\" src=\"http://sunweilun.github.io/images/paper/sketch2scene_thumb.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eAction-Driven 3D Indoor Scene Evolution (2016)\u003c/b\u003e [[Paper]](https://www.cs.sfu.ca/~haoz/pubs/ma_siga16_action.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://maruitx.github.io/project/adise/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eThe Clutterpalette: An Interactive Tool for Detailing Indoor Scenes (2015)\u003c/b\u003e [[Paper]](https://www.cs.umb.edu/~craigyu/papers/clutterpalette.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/The%20Clutterpalette-%20An%20Interactive%20Tool%20for%20Detailing%20Indoor%20Scenes.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eImage2Scene: Transforming Style of 3D Room (2015)\u003c/b\u003e [[Paper]](https://dl.acm.org/doi/abs/10.1145/2733373.2806274)\n\u003cp align=\"center\"\u003e\u003cimg width=\"60%\" src=\"imgs/Image2Scene.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eRelationship Templates for Creating Scene Variations (2016)\u003c/b\u003e [[Paper]](http://geometry.cs.ucl.ac.uk/projects/2016/relationship-templates/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://geometry.cs.ucl.ac.uk/projects/2016/relationship-templates/paper_docs/teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eIM2CAD (2017)\u003c/b\u003e [[Paper]](http://homes.cs.washington.edu/~izadinia/im2cad.html)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://i.imgur.com/KhtOeuB.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003ePredicting Complete 3D Models of Indoor Scenes (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1504.02437.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Predicting%20Complete%203D%20Models%20of%20Indoor%20Scenes.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eComplete 3D Scene Parsing from Single RGBD Image (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1710.09490.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Complete%203D%20Scene%20Parsing%20from%20Single%20RGBD%20Image.jpeg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eRaster-to-Vector: Revisiting Floorplan Transformation (2017, ICCV)\u003c/b\u003e [[Paper]](http://www.cse.wustl.edu/~chenliu/floorplan-transformation.html) [[Code]](https://github.com/art-programmer/FloorplanTransformation)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://www.cse.wustl.edu/~chenliu/floorplan-transformation/teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eFully Convolutional Refined Auto-Encoding Generative Adversarial Networks for 3D Multi Object Scenes (2017)\u003c/b\u003e [[Blog]](https://becominghuman.ai/3d-multi-object-gan-7b7cee4abf80)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://cdn-images-1.medium.com/max/1600/1*NckW2hfgbHhEP3P8Z5ZLjQ.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eAdaptive Synthesis of Indoor Scenes via Activity-Associated Object Relation Graphs (2017 SIGGRAPH Asia)\u003c/b\u003e [[Paper]](http://arts.buaa.edu.cn/projects/sa17/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://sa2017.siggraph.org/images/events/c121-e45-publicimage.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eAutomated Interior Design Using a Genetic Algorithm (2017)\u003c/b\u003e [[Paper]](https://publik.tuwien.ac.at/files/publik_262718.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://www.peterkan.com/pictures/teaserq.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eSceneSuggest: Context-driven 3D Scene Design (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1703.00061.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/SceneSuggest%20-Context-driven%203D%20Scene%20Design%20(2017).png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eA fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1703.04699v1.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/A%20fully%20end-to-end%20deep%20learning%20approach%20for%20real-time%20simultaneous%203D%20reconstruction%20and%20material%20recognition%20(2017).png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eHuman-centric Indoor Scene Synthesis Using Stochastic Grammar (2018, CVPR)\u003c/b\u003e[[Paper]](http://web.cs.ucla.edu/~syqi/publications/cvpr2018synthesis/cvpr2018synthesis.pdf) [[Supplementary]](http://web.cs.ucla.edu/~syqi/publications/cvpr2018synthesis/cvpr2018synthesis_supplementary.pdf) [[Code]](https://github.com/SiyuanQi/human-centric-scene-synthesis)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://web.cs.ucla.edu/~syqi/publications/thumbnails/cvpr2018synthesis.gif\" /\u003e\u003c/p\u003e\n\n:camera::game_die: \u003cb\u003eFloorNet: A Unified Framework for Floorplan Reconstruction from 3D Scans (2018)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1804.00090.pdf) [[Code]](http://art-programmer.github.io/floornet.html)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://art-programmer.github.io/floornet/teaser.png\" /\u003e\u003c/p\u003e\n\n:space_invader: \u003cb\u003eScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans (2018)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1712.10215.pdf) \n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://niessnerlab.org/papers/2018/3scancomplete/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eDeep Convolutional Priors for Indoor Scene Synthesis (2018)\u003c/b\u003e [[Paper]](https://kwang-ether.github.io/pdf/deepsynth.pdf) \n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://msavva.github.io/files/deepsynth.png\" /\u003e\u003c/p\u003e\n\n:camera: \u003cb\u003eFast and Flexible Indoor scene synthesis via Deep Convolutional Generative Models (2018)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1811.12463.pdf) [[Code]](https://github.com/brownvc/fast-synth)\n\u003cp align=\"center\"\u003e\u003cimg width=\"80%\" src=\"imgs/Fast%20and%20Flexible%20Indoor%20scene%20synthesis%20via%20Deep%20Convolutional%20Generative%20Models.jpg\" \u003e\u003c/p\u003e\n\n\u003cb\u003eConfigurable 3D Scene Synthesis and 2D Image Rendering\nwith Per-Pixel Ground Truth using Stochastic Grammars (2018)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1704.00112.pdf) \n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://media.springernature.com/original/springer-static/image/art%3A10.1007%2Fs11263-018-1103-5/MediaObjects/11263_2018_1103_Fig5_HTML.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eHolistic 3D Scene Parsing and Reconstruction from a Single RGB Image (ECCV 2018)\u003c/b\u003e [[Paper]](http://siyuanhuang.com/holistic_parsing/main.html) \n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://web.cs.ucla.edu/~syqi/publications/thumbnails/eccv2018scene.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eLanguage-Driven Synthesis of 3D Scenes from Scene Databases (SIGGRAPH Asia 2018)\u003c/b\u003e [[Paper]](http://www.sfu.ca/~agadipat/publications/2018/T2S/project_page.html) \n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://www.sfu.ca/~agadipat/publications/2018/T2S/teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eDeep Generative Modeling for Scene Synthesis via Hybrid Representations (2018)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1808.02084.pdf) \n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Deep%20Generative%20Modeling%20for%20Scene%20Synthesis%20via%20Hybrid%20Representations%20(2018).jpeg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eGRAINS: Generative Recursive Autoencoders for INdoor Scenes (2018)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1807.09193.pdf) \n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://www.groundai.com/media/arxiv_projects/373503/new_pics/teaserfig.jpg.750x0_q75_crop.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eSEETHROUGH: Finding Objects in Heavily Occluded Indoor Scene Images (2018)\u003c/b\u003e [[Paper]](http://www.vovakim.com/papers/18_3DVOral_SeeThrough.pdf) \n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://geometry.cs.ucl.ac.uk/projects/2018/seethrough/paper_docs/result_plate.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003e:space_invader: Scan2CAD: Learning CAD Model Alignment in RGB-D Scans (CVPR 2019)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1811.11187.pdf) [[Code]](https://github.com/skanti/Scan2CAD)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://www.niessnerlab.org/papers/2019/5scan2cad/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003e:gem: Scan2Mesh: From Unstructured Range Scans to 3D Meshes (CVPR 2019)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1811.10464.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://www.niessnerlab.org/papers/2019/4scan2mesh/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003e:space_invader: 3D-SIC: 3D Semantic Instance Completion for RGB-D Scans (arXiv 2019)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1904.12012.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://www.niessnerlab.org/papers/2019/z1sic/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003e:space_invader: End-to-End CAD Model Retrieval and 9DoF Alignment in 3D Scans (arXiv 2019)\u003c/b\u003e [[Paper]](https://arxiv.org/abs/1906.04201)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://www.niessnerlab.org/papers/2019/z2end2end/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eA Survey of 3D Indoor Scene Synthesis (2020) \u003c/b\u003e [[Paper]](https://www.researchgate.net/profile/Shao_Kui_Zhang/publication/333135099_A_Survey_of_3D_Indoor_Scene_Synthesis/links/5ce13a5492851c4eabad4de0/A-Survey-of-3D-Indoor-Scene-Synthesis.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"60%\" src=\"https://github.com/julyrashchenko/3D-Machine-Learning/blob/master/imgs/A%20Survey%20of%203D%20Indoor%20Scene%20Synthesis.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003e:pill: :camera: PlanIT: Planning and Instantiating Indoor Scenes with Relation Graph and Spatial Prior Networks (2019) \u003c/b\u003e [[Paper]](https://kwang-ether.github.io/pdf/planit.pdf) [[Code]](https://github.com/brownvc/planit)\n\u003cp align=\"center\"\u003e\u003cimg src=\"imgs/PlanIT.jpg\"\u003e\u003c/p\u003e\n\n\u003cb\u003e:space_invader: Feature-metric Registration: A Fast Semi-Supervised Approach for Robust Point Cloud Registration without Correspondences (CVPR 2020)\u003c/b\u003e [[Paper]](https://arxiv.org/abs/2005.01014)[[Code]](https://github.com/XiaoshuiHuang/fmr)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/XiaoshuiHuang/xiaoshuihuang.github.io/blob/master/research/2020-feature-metric.png?raw=true\" /\u003e\u003c/p\u003e\n\n\u003cb\u003e:pill: Human-centric metrics for indoor scene assessment and synthesis (2020) \u003c/b\u003e [[Paper]](sciencedirect.com/science/article/abs/pii/S1524070320300175)\n\u003cp align=\"center\"\u003e\u003cimg width=\"60%\" src=\"imgs/Human-centric%20metrics%20for%20indoor%20scene%20assessment%20and%20synthesis.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003e SceneCAD: Predicting Object Alignments and Layouts in RGB-D Scans (2020) \u003c/b\u003e [[Paper]](https://arxiv.org/pdf/2003.12622.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"60%\" src=\"imgs/SceneCAD.jpg\" /\u003e\u003c/p\u003e\n\n\n\n\n\n\u003ca name=\"scene_understanding\" /\u003e\n\n## Scene Understanding (Another more detailed [repository](https://github.com/bertjiazheng/awesome-scene-understanding))\n\n\u003cb\u003eRecovering the Spatial Layout of Cluttered Rooms (2009)\u003c/b\u003e [[Paper]](http://dhoiem.cs.illinois.edu/publications/iccv2009_hedau_indoor.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"60%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Recovering%20the%20Spatial%20Layout%20of%20Cluttered%20Rooms.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eCharacterizing Structural Relationships in Scenes Using Graph Kernels (2011 SIGGRAPH)\u003c/b\u003e [[Paper]](https://graphics.stanford.edu/~mdfisher/graphKernel.html)\n\u003cp align=\"center\"\u003e\u003cimg width=\"60%\" src=\"https://graphics.stanford.edu/~mdfisher/papers/graphKernelTeaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eUnderstanding Indoor Scenes Using 3D Geometric Phrases (2013)\u003c/b\u003e [[Paper]](http://cvgl.stanford.edu/projects/3dgp/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"30%\" src=\"http://cvgl.stanford.edu/projects/3dgp/images/title.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eOrganizing Heterogeneous Scene Collections through Contextual Focal Points (2014 SIGGRAPH)\u003c/b\u003e [[Paper]](http://kevinkaixu.net/projects/focal.html)\n\u003cp align=\"center\"\u003e\u003cimg width=\"60%\" src=\"http://kevinkaixu.net/projects/focal/overlapping_clusters.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eSceneGrok: Inferring Action Maps in 3D Environments (2014, SIGGRAPH)\u003c/b\u003e [[Paper]](http://graphics.stanford.edu/projects/scenegrok/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://graphics.stanford.edu/projects/scenegrok/scenegrok.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003ePanoContext: A Whole-room 3D Context Model for Panoramic Scene Understanding (2014)\u003c/b\u003e [[Paper]](http://panocontext.cs.princeton.edu/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://panocontext.cs.princeton.edu/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eLearning Informative Edge Maps for Indoor Scene Layout Prediction (2015)\u003c/b\u003e [[Paper]](http://slazebni.cs.illinois.edu/publications/iccv15_informative.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Learning%20Informative%20Edge%20Maps%20for%20Indoor%20Scene%20Layout%20Prediction.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eRent3D: Floor-Plan Priors for Monocular Layout Estimation (2015)\u003c/b\u003e [[Paper]](http://www.cs.toronto.edu/~fidler/projects/rent3D.html)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://www.cs.toronto.edu/~fidler/projects/layout-res.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eA Coarse-to-Fine Indoor Layout Estimation (CFILE) Method (2016)\u003c/b\u003e [[Paper]](https://pdfs.semanticscholar.org/7024/a92186b81e6133dc779f497d06877b48d82b.pdf?_ga=2.54181869.497995160.1510977308-665742395.1510465328)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/A%20Coarse-to-Fine%20Indoor%20Layout%20Estimation%20(CFILE)%20Method%20(2016).png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eDeLay: Robust Spatial Layout Estimation for Cluttered Indoor Scenes (2016)\u003c/b\u003e [[Paper]](http://deeplayout.stanford.edu/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"30%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/DeLay-Robust%20Spatial%20Layout%20Estimation%20for%20Cluttered%20Indoor%20Scenes.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003e3D Semantic Parsing of Large-Scale Indoor Spaces (2016)\u003c/b\u003e [[Paper]](http://buildingparser.stanford.edu/method.html) [[Code]](https://github.com/alexsax/2D-3D-Semantics)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://buildingparser.stanford.edu/images/teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eSingle Image 3D Interpreter Network (2016)\u003c/b\u003e [[Paper]](http://3dinterpreter.csail.mit.edu/) [[Code]](https://github.com/jiajunwu/3dinn)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://3dinterpreter.csail.mit.edu/images/spotlight_3dinn_large.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eDeep Multi-Modal Image Correspondence Learning (2016)\u003c/b\u003e [[Paper]](http://www.cse.wustl.edu/~chenliu/floorplan-matching.html)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://art-programmer.github.io/floorplan-matching/teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003ePhysically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks (2017)\u003c/b\u003e [[Paper]](http://3dvision.princeton.edu/projects/2016/PBRS/) [[Code]](https://github.com/yindaz/pbrs) [[Code]](https://github.com/yindaz/surface_normal) [[Code]](https://github.com/fyu/dilation) [[Code]](https://github.com/s9xie/hed)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://pbs.twimg.com/media/C0YERJOXEAA69xN.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eRoomNet: End-to-End Room Layout Estimation (2017)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1703.06241.pdf)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://pbs.twimg.com/media/C7Z29GsV0AASEvR.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eSUN RGB-D: A RGB-D Scene Understanding Benchmark Suite (2017)\u003c/b\u003e [[Paper]](http://rgbd.cs.princeton.edu/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://rgbd.cs.princeton.edu/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eSemantic Scene Completion from a Single Depth Image (2017)\u003c/b\u003e [[Paper]](http://sscnet.cs.princeton.edu/) [[Code]](https://github.com/shurans/sscnet)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://sscnet.cs.princeton.edu/teaser.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eFactoring Shape, Pose, and Layout  from the 2D Image of a 3D Scene (2018 CVPR)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1712.01812.pdf) [[Code]](https://shubhtuls.github.io/factored3d/)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://shubhtuls.github.io/factored3d/resources/images/teaser.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eLayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image (2018 CVPR)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1803.08999.pdf) [[Code]](https://github.com/zouchuhang/LayoutNet)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://p0.ifengimg.com/pmop/2018/0404/A1D0CAE48130C918FE624FA60495F237C67172F6_size63_w797_h755.jpeg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003ePlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image (2018 CVPR)\u003c/b\u003e [[Paper]](http://art-programmer.github.io/planenet/paper.pdf) [[Code]](http://art-programmer.github.io/planenet.html)\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://art-programmer.github.io/images/planenet.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eCross-Domain Self-supervised Multi-task Feature Learning using Synthetic Imagery (2018 CVPR)\u003c/b\u003e [[Paper]](http://web.cs.ucdavis.edu/~yjlee/projects/cvpr2018.pdf) \u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://jason718.github.io/project/cvpr18/files/concept_pic.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003ePano2CAD: Room Layout From A Single Panorama Image (2018 CVPR)\u003c/b\u003e [[Paper]](http://bjornstenger.github.io/papers/xu_wacv2017.pdf) \u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://www.groundai.com/media/arxiv_projects/58924/figures/Compare_2b.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eAutomatic 3D Indoor Scene Modeling from Single Panorama (2018 CVPR)\u003c/b\u003e [[Paper]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_Automatic_3D_Indoor_CVPR_2018_paper.pdf) \u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Automatic%203D%20Indoor%20Scene%20Modeling%20from%20Single%20Panorama%20(2018%20CVPR).jpeg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eSingle-Image Piece-wise Planar 3D Reconstruction via Associative Embedding (2019 CVPR)\u003c/b\u003e [[Paper]](https://arxiv.org/pdf/1902.09777.pdf) [[Code]](https://github.com/svip-lab/PlanarReconstruction) \u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://github.com/svip-lab/PlanarReconstruction/blob/master/misc/pipeline.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003e3D-Aware Scene Manipulation via Inverse Graphics (NeurIPS 2018)\u003c/b\u003e [[Paper]](http://3dsdn.csail.mit.edu/) [[Code]](https://github.com/svip-lab/PlanarReconstruction) \u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"http://3dsdn.csail.mit.edu/images/teaser.png\" /\u003e\u003c/p\u003e\n\n:gem: \u003cb\u003e3D Scene Reconstruction with Multi-layer Depth and Epipolar Transformers (ICCV 2019)\u003c/b\u003e [[Paper]](https://research.dshin.org/iccv19/multi-layer-depth/) \u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://research.dshin.org/iccv19/multi-layer-depth/figures/overview_1.png\" /\u003e\u003cbr\u003e\u003cimg width=\"50%\" src=\"https://research.dshin.org/iccv19/multi-layer-depth/figures/voxelization00.jpg\" /\u003e\u003c/p\u003e\n\n\u003cb\u003ePerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points (NIPS 2019)\u003c/b\u003e [[Paper]](https://papers.nips.cc/paper/9093-perspectivenet-3d-object-detection-from-a-single-rgb-image-via-perspective-points.pdf) \u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://storage.googleapis.com/groundai-web-prod/media/users/user_288036/project_402358/images/x1.png\" /\u003e\u003c/p\u003e\n\n\u003cb\u003eHolistic++ Scene Understanding: Single-view 3D Holistic Scene Parsing and Human Pose Estimation with Human-Object Interaction and Physical Commonsense (ICCV 2019)\u003c/b\u003e [[Paper \u0026 Code]](https://github.com/yixchen/holistic_scene_human) \u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"https://yixchen.github.io/holisticpp/file/pg.png\" /\u003e\u003c/p\u003e\n","funding_links":[],"categories":["Repos","Others","Uncategorized","资源","Computer Vision","Awesome-Game","Awesome Lists","Table of Contents","其他_机器视觉","miscellaneous","Related Resources","👩🏽‍💻Development [^](#table)","Related","3D","Bachelor-Level","Vision","[Other Awesome Lists](#awesome-3d-multi-object-tracking)"],"sub_categories":["Research Tools","Uncategorized","多相机拼接","Explainability, Interpretability and Fairness","网络服务_其他","Related Awesome Lists [^](#table)","Other","B.Sc.: Bonusliste","2020"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftimzhang642%2F3D-Machine-Learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftimzhang642%2F3D-Machine-Learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftimzhang642%2F3D-Machine-Learning/lists"}