{"id":40277,"url":"https://github.com/scott89/awesome-depth","name":"awesome-depth","description":"A curated list of publication for depth estimation","projects_count":72,"last_synced_at":"2026-07-12T07:00:27.176Z","repository":{"id":52193509,"uuid":"140248672","full_name":"scott89/awesome-depth","owner":"scott89","description":"A curated list of publication for depth estimation","archived":false,"fork":false,"pushed_at":"2020-09-30T08:25:06.000Z","size":62,"stargazers_count":337,"open_issues_count":0,"forks_count":36,"subscribers_count":13,"default_branch":"master","last_synced_at":"2026-06-24T03:03:58.277Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"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/scott89.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}},"created_at":"2018-07-09T07:39:41.000Z","updated_at":"2026-04-04T20:40:52.000Z","dependencies_parsed_at":"2022-09-06T05:41:42.421Z","dependency_job_id":null,"html_url":"https://github.com/scott89/awesome-depth","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/scott89/awesome-depth","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scott89%2Fawesome-depth","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scott89%2Fawesome-depth/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scott89%2Fawesome-depth/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scott89%2Fawesome-depth/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/scott89","download_url":"https://codeload.github.com/scott89/awesome-depth/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scott89%2Fawesome-depth/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35384619,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-07-12T02:00:06.386Z","response_time":87,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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"}},"created_at":"2024-01-13T21:17:52.141Z","updated_at":"2026-07-12T07:00:27.177Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["1. Monocular Depth (Fully Supervised)","2. Monocular Depth (Semi- / Un-Supervised)","3. Depth Completion/Super-resolution","0. Survey","4. Depth Fusion","5. Depth Dataset","6. 3D Photography","8. Optical Flow \u0026 Scene Flow"],"sub_categories":["2.2 Multi View","2.1 Stereo Consistency"],"readme":"# awesome-depth\n\nA curated list of publication for depth estimation\n\n## 0. Survey \n[1] Dijk et al, How do neural networks see depth in single images? [PDF](https://arxiv.org/pdf/1905.07005.pdf)\n\n[2] Amlaan et al, Monocluar depth estimation: A survey, [PDF](https://arxiv.org/pdf/1901.09402.pdf)\n\n[3] Zhao et al, Monocular Depth Estimation Based On Deep Learning: An Overview, [PDF](https://arxiv.org/pdf/2003.06620.pdf)\n\n## 1. Monocular Depth (Fully Supervised)\n\n[1] Eigen et al, Depth Map Prediction from a Single Image using a Multi-Scale Deep Network, NIPS 2014, [Web](https://cs.nyu.edu/~deigen/depth/)\n\n[2] Eigen et al, Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture, ICCV 2015, [Web](https://cs.nyu.edu/~deigen/dnl/)\n\n[3] Laina et al, Deeper Depth Prediction with Fully Convolutional Residual Networks, 3DV 2016, [Code](https://github.com/iro-cp/FCRN-DepthPrediction)\n\n[4] Chen et al, Single-Image Depth Perception in the Wild, NIPS 2016, [Web](http://www-personal.umich.edu/~wfchen/depth-in-the-wild/)\n\n[5] Li et al, A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images, ICCV 2017, [PDF](http://arxiv.org/abs/1607.00730)\n\n[6] Xu et al, Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation, CVPR 2018, [PDF](https://arxiv.org/abs/1803.11029)\n\n[7] Xu et al, PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network, CVPR 2018, [PDF](https://arxiv.org/abs/1805.04409)\n\n[8] Qi et al, GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation, CVPR 2018, [PDF](https://xjqi.github.io/geonet.pdf)\n\n[9] Fu et al, Deep Ordinal Regression Network for Monocular Depth Estimation, CVPR 2018, [PDF](https://arxiv.org/abs/1806.02446)\n\n[10] Zhang et al, Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation, ECCV 2018, [PDF](http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhenyu_Zhang_Joint_Task-Recursive_Learning_ECCV_2018_paper.pdf)\n\n[11] Jiao et al, Look Deeper into Depth: Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss, ECCV 2018, [PDF](http://openaccess.thecvf.com/content_ECCV_2018/papers/Jianbo_Jiao_Look_Deeper_into_ECCV_2018_paper.pdf)\n\n[12] Cheng et al, Depth Esimation via Affinity Learning with Convolutional Spatial Propagation Network, ECCV 2018, [PDF](http://openaccess.thecvf.com/content_ECCV_2018/papers/Xinjing_Cheng_Depth_Estimation_via_ECCV_2018_paper.pdf), [Code](https://github.com/XinJCheng/CSPN)\n\n[13] Lee et al, Monocular depth estimation using relative depth maps, CVPR 2019, [PDF](http://openaccess.thecvf.com/content_CVPR_2019/papers/Lee_Monocular_Depth_Estimation_Using_Relative_Depth_Maps_CVPR_2019_paper.pdf),  [Code](https://github.com/jaehanlee-mcl/monocular-depth-estimation-using-relative-depth-maps)\n\n[14] Lee et al, From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation, Arxiv, [PDF](https://arxiv.org/pdf/1907.10326.pdf), [Code](https://github.com/cogaplex-bts/bts)\n\n[15] Zhang et al, Pattern-Affinitive Propagation across Depth, Surface Normal and Semantic Segmentation, CVPR 2019, [PDF](https://arxiv.org/pdf/1906.03525v1.pdf)\n\n[16] Zhang et al, Exploiting temporal consistency for real-time video depth estimation, ICCV 2019, [PDF](https://arxiv.org/pdf/1908.03706.pdf), [Code](https://github.com/hkzhang91/ST-CLSTM)\n\n\n## 2. Monocular Depth (Semi- / Un-Supervised)\n\n### 2.1 Stereo Consistency\n\n[1] Garg et al, Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue, ECCV 2016,  [Code](https://github.com/Ravi-Garg/Unsupervised_Depth_Estimation)\n\n[2] Godard et al, Unsupervised Monocular Depth Estimation with Left-Right Consistency, CVPR 2017, [Web](http://visual.cs.ucl.ac.uk/pubs/monoDepth/)\n\n[3] Kuznietsov et al, Semi-Supervised Deep Learning for Monocular Depth Map Prediction, CVPR 2017, [Code](https://github.com/Yevkuzn/semodepth)\n\n[4] Luo et al, Single View Stereo Matching, CVPR 2018, [Code](https://github.com/lawy623/SVS)\n\n[5] Godard et al, Digging Into Self-Supervised Monocular Depth Estimation, aXiv 2018, [PDF](https://arxiv.org/abs/1711.07933)\n\n[6] Lai et al, Bridging Stereo Matching and Optical Flow via Spatio temporal, CVPR 2019, [PDF](https://arxiv.org/pdf/1905.09265.pdf), [Code](https://github.com/lelimite4444/)\n\n[7] Tosi et al, Learning monocular depth estimation infusing traditional stereo knowledge, CVPR 2019, [PDF](https://arxiv.org/pdf/1904.04144.pdf) [Code](https://github.com/fabiotosi92/monoResMatch-Tensorflow)\n\n[8] Garg et al, Learning Single Camera Depth Estimation using Dual-Pixels, ICCV 2019, [PDF](https://arxiv.org/pdf/1904.05822.pdf) [Code](https://github.com/google-research/google-research/tree/master/dual_pixels)\n\n[9] Zhang et al, Du2Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels, arXiv 2020, [PDF](https://arxiv.org/pdf/2003.14299.pdf)\n\n\n### 2.2 Multi View\n\n[1] Zhou et al, Unsupervised Learning of Depth and Ego-Motion from Video, CVPR 2017, [Web](https://people.eecs.berkeley.edu/~tinghuiz/projects/SfMLearner/)\n\n[2] Im et al, Robust Depth Estimation from Auto Bracketed Images, CVPR 2018, [PDF](https://arxiv.org/abs/1803.07702)\n\n[3] Yin et al, GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose, CVPR 2018,[Code](https://github.com/yzcjtr/GeoNet)\n\n[4] Wang et al, Learning Depth from Monocular Videos using Direct Methods, CVPR 2018, [Code](https://github.com/MightyChaos/LKVOLearner)\n\n[5] Yang et al, LEGO: Learning Edge with Geometry all at Once by Watching Videos, CVPR 2018, [Code](https://github.com/zhenheny/LEGO)\n\n[6] Mahjourian et al, Unsupervised Learning of Depth and Ego-Motion from Monocular Video\nUsing 3D Geometric Constraints, CVPR 2018, [PDF](https://arxiv.org/abs/1802.05522)\n\n[7] Zhan et al, Unsupervised Learning of Monocular Depth Estimation and Visual Odometry\nwith Deep Feature Reconstruction, CVPR 2018, [Web](https://github.com/Huangying-Zhan/Depth-VO-Feat)\n\n[8] Ran et al, Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation, CVPR 2019, [PDF](https://arxiv.org/pdf/1805.09806.pdf), [Code](https://github.com/anuragranj/cc)\n\n[9] Bian et al, Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video, NIPS 2019, [PDF](https://arxiv.org/pdf/1908.10553.pdf), [Code](https://github.com/JiawangBian/SC-SfMLearner-Release)\n \n[10] Luo et al, Consistent Video Depth Estimation, SIGGRAPH 2020, [Web](https://roxanneluo.github.io/Consistent-Video-Depth-Estimation/)\n\n[11] Teed et al, DEEPV2D: VIDEO TO DEPTH WITH DIFFERENTIABLE STRUCTURE FROM MOTION, ICLR 2020, [PDF](https://openreview.net/pdf?id=HJeO7RNKPr) [Code](https://github.com/princeton-vl/DeepV2D)\n\n[12] Guizilini et al, 3D Packing for Self-Supervised Monocular Depth Estimation, CVPR 2020, [PDF](https://arxiv.org/pdf/1905.02693.pdf) [Code](https://github.com/TRI-ML/packnet-sfm)\n\n[13] Zhao et al, Towards Better Generalization: Joint Depth-Pose Learning without PoseNet, CVPR 2020 [PDF](https://arxiv.org/abs/2004.01314) [Code](https://github.com/B1ueber2y/TrianFlow)\n\n[14] Bian et al, Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video, NeurIPs 2019 [PDF](https://arxiv.org/abs/1908.10553) [Code](https://github.com/JiawangBian/SC-SfMLearner-Release)\n\n## 3. Depth Completion/Super-resolution\n\n[1] Cheng et al, Learning Depth with Convolutional Spatial Propagation Network, arXiv 2018, [PDF](https://arxiv.org/pdf/1810.02695.pdf), [Code](https://github.com/XinJCheng/CSPN)\n\n[2] Zhang et al, Deep Depth Completion of a Single RGB-D Image, CVPR 2018, [PDF](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Deep_Depth_Completion_CVPR_2018_paper.pdf)\n\n[3] Qiu et al, DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image, CVPR 2019, [PDF](http://openaccess.thecvf.com/content_CVPR_2019/papers/Qiu_DeepLiDAR_Deep_Surface_Normal_Guided_Depth_Prediction_for_Outdoor_Scene_CVPR_2019_paper.pdf), [Code](https://github.com/JiaxiongQ/DeepLiDAR)\n\n[4] Chen et al, Learning Joint 2D-3D Representations for Depth Completion, ICCV 2019, [PDF](http://openaccess.thecvf.com/content_ICCV_2019/papers/Chen_Learning_Joint_2D-3D_Representations_for_Depth_Completion_ICCV_2019_paper.pdf)\n\n[5] Tang et al, Learning Guided Convolutional Network for Depth Completion, arXiv 2019, [PDF](https://arxiv.org/pdf/1908.01238.pdf),  [Code](https://github.com/kakaxi314/GuideNet)\n\n## 4. Depth Fusion\n\n[1] Marin et al, Reliable Fusion of ToF and Stereo Depth Driven by Confidence Measures, ECCV 2016, [PDF](https://lttm.dei.unipd.it//paper_data/eccv16/LCfusion-eccv16.pdf)\n\n[2] Agresti et al, Deep Learning for Confidence Information in Stereo and ToF Data Fusion, ICCVW 2017, [Web](https://lttm.dei.unipd.it/paper_data/deepfusion/)\n\n\n## 5. Depth Dataset\n\n[1] Srinivasan et al, Aperture Supervision for Monocular Depth Estimation, CVPR 2018, [Code](https://github.com/google/aperture_supervision)\n\n[2] Li et al, MegaDepth: Learning Single-View Depth Prediction from Internet Photos, CVPR 2018, [Web](http://www.cs.cornell.edu/projects/megadepth/)\n\n[3] Monocular Relative Depth Perception with Web Stereo Data Supervision, CVPR 2018, [PDF](http://openaccess.thecvf.com/content_cvpr_2018/papers/Xian_Monocular_Relative_Depth_CVPR_2018_paper.pdf)\n\n[4] Li et al, Learning the depths of moving people by watching frozen people, CVPR 2019, [Web](https://mannequin-depth.github.io/)\n\n[5] Chen et al, Learning Single-Image Depth from Videos using Quality Assessment Networks, CVPR 2019, [PDF](http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Learning_Single-Image_Depth_From_Videos_Using_Quality_Assessment_Networks_CVPR_2019_paper.pdf)\n\n[6] Li et al, Learning the Depths of Moving People by Watching Frozen People, CVPR 2019(oral), [PDF](https://arxiv.org/pdf/1904.11111.pdf)\n\n[7] See [Link](https://scott89.github.io/depth-talk/#/6/1) for more conventional data sets.\n\n\n\n## 6. 3D Photography \n\n[1] Zhou et al, Stereo Magnification: Learning view synthesis using multiplane images, SIGGRAPH 2018, [Web](https://people.eecs.berkeley.edu/~tinghuiz/projects/mpi/)\n\n[2] Niklaus et al, 3D Ken Burns Effect from a Single Image, SIGGRAPH 2019, [PDF](https://arxiv.org/pdf/1909.05483.pdf) [Code](https://github.com/sniklaus/3d-ken-burns)\n\n[3] Wiles et al, SynSin: End-to-end View Synthesis from a Single Image, CVPR 2020, [Web](http://www.robots.ox.ac.uk/~ow/synsin.html)\n\n[4] Shih et al, 3D Photography using Context-aware Layered Depth Inpainting, CVPR 2020, [Web](https://shihmengli.github.io/3D-Photo-Inpainting/)\n\n[5] Mildenhall et al, Representing Scenes as Neural Radiance Fields for View Synthesis, arXiv 2020, [Web](http://www.matthewtancik.com/nerf)\n\n[6] Chen et al, Monocular Neural Image Based Rendering with Continuous View Control, ICCV 2019, [Web](https://ait.ethz.ch/projects/2019/cont-view-synth/)\n\n[7] Chen et al, Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction, AAAI 2018, [PDF](https://arxiv.org/pdf/1706.07036.pdf)\n\n[8] Kopf et al, One Shot 3D Photography, SIGGRAPH 2020 [PDF](https://arxiv.org/pdf/2008.12298.pdf) [Web](https://facebookresearch.github.io/one_shot_3d_photography/)\n\n## 7. RGB-D Application\n\n## 8. Optical Flow \u0026 Scene Flow\n\n[1] Dosovitskiy et al, FlowNet: Learning optical flow with convolutional networks, CVPR 2015, [PDF](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Dosovitskiy_FlowNet_Learning_Optical_ICCV_2015_paper.pdf)\n\n[2] Yu et al, Back to basics: Unsupervised learning of optical flow via brightness constancy and motion smoothness, ECCV 2016 Workshop, [PDF](https://arxiv.org/pdf/1608.05842v1.pdf)\n\n[3] Bailer et al, CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss, CVPR 2017, [PDF](http://arxiv.org/abs/1607.08064)\n\n[4] Ranjan et al, Optical Flow Estimation using a Spatial Pyramid Network(SpyNet), CVPR 2017, [Code](https://github.com/anuragranj/spynet)\n\n[5] Ilg et al, FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks, CVPR 2017, [Code](https://github.com/lmb-freiburg/flownet2)\n\n[6] Sun et al, PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume, CVPR 2018, [Code](https://github.com/NVlabs/PWC-Net)\n\n[7] Wang et al, Occlusion Aware Unsupervised Learning of Optical Flow, CVPR 2018, [PDF](http://arxiv.org/abs/1711.05890)\n\n[6] Hui et al, LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation, CVPR 2018, [PDF](http://openaccess.thecvf.com/content_cvpr_2018/papers/Hui_LiteFlowNet_A_Lightweight_CVPR_2018_paper.pdf)\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/scott89%2Fawesome-depth/projects"}