awesome-mvs
A curated list of tutorials, papers, software related to multi-view stereo.
https://github.com/krahets/awesome-mvs
Last synced: 2 days ago
JSON representation
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Paper
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Multi-View Stereo
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Reliable surface reconstruction from multiple range images
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- **SurfaceNet+:** An end-to-end 3D neural network for very sparse multi-view stereopsis
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Multi-view stereo revisited
- Multi-view stereo for community photo collections
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Towards high-resolution large-scale multi-view stereo
- Towards internet-scale multi-view stereo
- PMVS
- A Theory of Shape by Space Carving
- **MVE**-A Multi-View Reconstruction Environment
- Patchmatch based joint view selection and depthmap estimation
- **TAPA-MVS:** Textureless-aware patchmatch multi-view stereo
- Pyramid multi‐view stereo with local consistency
- **(ACMM)** Multi-scale geometric consistency guided multi-view stereo
- **(ACMP)** Planar Prior Assisted PatchMatch Multi-View Stereo
- **DP-MVS:** Detail Preserving Multi-View Surface Reconstruction of Large-Scale Scenes
- **MVSNet:** Depth Inference for Unstructured Multi-view Stereo
- **MVDepthNet:** Real-time multiview depth estimation neural network
- **Recurrent MVSNet** for high-resolution multi-view stereo depth inference
- **DPSNet:** End-to-end deep plane sweep stereo
- **P-MVSNet:** Learning patch-wise matching confidence aggregation for multi-view stereo
- **(PointMVSNet)** Point-based Multi-view Stereo Network
- Pyramid multi-view stereo net with self-adaptive view aggregation
- **(CasMVSNet)** Cascade cost volume for high-resolution multi-view stereo and stereo matching
- **(CVP-MVSNet)** Cost volume pyramid based depth inference for multi-view stereo
- **Fast-MVSNet:** Sparse-to-dense multi-view stereo with learned propagation and gauss-newton refinement
- **(AttMVS)** Attention-aware multi-view stereo
- **(Vis-MVSNet)** Visibility-aware multi-view stereo network
- Visibility-aware point-based multi-view stereo network
- **PVSNet:** Pixelwise visibility-aware multi-view stereo network
- **BP-MVSNet:** Belief-propagation-layers for multi-view-stereo
- **DeepC-MVS:** Deep confidence prediction for multi-view stereo reconstruction
- Mesh-guided multi-view stereo with pyramid architecture
- **PatchmatchNet:** Learned multi-view patchmatch stereo
- **AA-RMVSNet:** Adaptive aggregation recurrent multi-view stereo network
- **PatchMatch-RL:** Deep MVS with Pixelwise Depth, Normal, and Visibility
- **EPP-MVSNet:** Epipolar-Assembling Based Depth Prediction for Multi-View Stereo
- Deep multi-view stereo gone wild
- **(GBiNet)** Generalized Binary Search Network for Highly-Efficient Multi-View Stereo
- **(UniMVSNet)** Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation
- **MVSTER:** Epipolar Transformer for Efficient Multi-View Stereo
- **TransMVSNet:** Global Context-aware Multi-view Stereo Network with Transformers
- Learning unsupervised multi-view stereopsis via robust photometric consistency
- **MVS2:** Deep unsupervised multi-view stereo with multi-view symmetry
- Self-supervised multi-view stereo via effective co-segmentation and data-augmentation
- Self-supervised Learning of Depth Inference for Multi-view Stereo
- Digging into Uncertainty in Self-supervised Multi-view Stereo
- **RC-MVSNet:** Unsupervised Multi-View Stereo with Neural Rendering
- Reliable surface reconstruction from multiple range images
- Consensus surfaces for modeling 3D objects from multiple range images
- **KinectFusion:** Real-time dense surface mapping and tracking
- **(VoxelHashing)** Real-time 3D reconstruction at scale using voxel hashing
- **ElasticFusion:** Dense SLAM Without A Pose Graph
- **BundleFusion:** real-time globally consistent 3D reconstruction using on-the-fly surface re-integration
- Learning a multi-view stereo machine
- **SurfaceNet:** An end-to-end 3d neural network for multiview stereopsis
- **Atlas:** End-to-end 3d scene reconstruction from posed images
- **RoutedFusion:** Learning real-time depth map fusion
- **SurfaceNet+:** An end-to-end 3D neural network for very sparse multi-view stereopsis
- **NeuralRecon:** Real-time coherent 3D reconstruction from monocular video
- **NeuralFusion:** Online depth fusion in latent space
- **PlanarRecon:** Real-time 3D Plane Detection and Reconstruction from Posed Monocular Videos
- **(DVR)** Differentiable Volumetric Rendering: Learning implicit 3d representations without 3d supervision
- **(IDR)** Multiview neural surface reconstruction by disentangling geometry and appearance
- **UNISURF:** Unifying neural implicit surfaces and radiance fields for multi-view reconstruction
- **NeuS:** Learning neural implicit surfaces by volume rendering for multi-view reconstruction
- **(VolSDF)** Volume rendering of neural implicit surfaces
- **NerfingMVS:** Guided optimization of neural radiance fields for indoor multi-view stereo
- **(ManhattanSDF)** Neural 3D Scene Reconstruction with the Manhattan-world Assumption
- **(NeuralRecon-W)** Neural 3D Reconstruction in the Wild
- **SurRF:** Unsupervised Multi-view Stereopsis by Learning Surface Radiance Field
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Reliable surface reconstruction from multiple range images
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Reliable surface reconstruction from multiple range images
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- **Mˆ3VSNet:** Unsupervised multi-metric multi-view stereo network
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- **DeepC-MVS:** Deep confidence prediction for multi-view stereo reconstruction
- Reliable surface reconstruction from multiple range images
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Pyramid multi-view stereo net with self-adaptive view aggregation
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Reliable surface reconstruction from multiple range images
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Reliable surface reconstruction from multiple range images
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Reliable surface reconstruction from multiple range images
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Reliable surface reconstruction from multiple range images
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Reliable surface reconstruction from multiple range images
- **MVDepthNet:** Real-time multiview depth estimation neural network
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Reliable surface reconstruction from multiple range images
- Efficient large-scale multi-view stereo for ultra high-resolution image sets
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Reliable surface reconstruction from multiple range images
- **(Vis-MVSNet)** Visibility-aware multi-view stereo network
- **Mˆ3VSNet:** Unsupervised multi-metric multi-view stereo network
- Reliable surface reconstruction from multiple range images
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Reliable surface reconstruction from multiple range images
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Reliable surface reconstruction from multiple range images
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Reliable surface reconstruction from multiple range images
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Reliable surface reconstruction from multiple range images
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Reliable surface reconstruction from multiple range images
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Reliable surface reconstruction from multiple range images
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Reliable surface reconstruction from multiple range images
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- Reliable surface reconstruction from multiple range images
- **NeuS:** Learning neural implicit surfaces by volume rendering for multi-view reconstruction
- Using multiple hypotheses to improve depth-maps for multi-view stereo
- **DPSNet:** End-to-end deep plane sweep stereo
- Pyramid multi-view stereo net with self-adaptive view aggregation
- **PVSNet:** Pixelwise visibility-aware multi-view stereo network
- **(UniMVSNet)** Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation
- Learning unsupervised multi-view stereopsis via robust photometric consistency
- Reliable surface reconstruction from multiple range images
- **BundleFusion:** real-time globally consistent 3D reconstruction using on-the-fly surface re-integration
- Reliable surface reconstruction from multiple range images
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Mesh Texturing
- Seamless image-based texture atlases using multi-band blending - P. Pons and R. Keriven. ICPR 2008.
- Texture Mapping for 3D Reconstruction with RGB-D Sensor
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Viewpoints and Trajectory Optimization
- Next Best View Planning for Active Model Improvement - Michael Frahm. BMVC 2009.
- Receding Horizon "Next-Best-View" Planner for 3D Exploration
- Submodular Trajectory Optimization for Aerial 3D Scanning
- Aerial path planning for urban scene reconstruction: A continuous optimization method and benchmark
- **Learn-to-Score:** Efficient 3D Scene Exploration by Predicting View Utility
- Automatic and semantically-aware 3D UAV flight planning for image-based 3D reconstruction
- Next-Best View Policy for 3D Reconstruction
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Survey
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- State of the art in high density image matching
- Deep Learning for Multi-View Stereo via Plane Sweep: A Survey
- Multi-view stereo in the Deep Learning Era: A comprehensive review
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- Deep Learning for Multi-View Stereo via Plane Sweep: A Survey
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
- On benchmarking camera calibration and multi-view stereo for high resolution imagery
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Tutorial
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Benchmark
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Viewpoints and Trajectory Optimization
- Multi-sensor large-scale dataset for multi-view 3D reconstruction
- **ETH3D**. A multi-view stereo benchmark with high-resolution images and multi-camera videos
- **ScanNet**: Richly-Annotated 3D Reconstructions of Indoor Scenes
- **BlendedMVS**: A large-scale dataset for generalized multi-view stereo networks
- **GigaMVS**: A Benchmark for Ultra-large-scale Gigapixel-level 3D Reconstruction
- Capturing, Reconstructing, and Simulating: the UrbanScene3D Dataset
- **DTU**. Large scale multi-view stereopsis evaluation - scale data for multiple-view stereopsis](https://publications.aston.ac.uk/id/eprint/28180/1/Large_scale_data_for_multiple_view_stereopsis.pdf). Aanæs, Henrik, et al. ICCV2016.
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Open Source
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Viewpoints and Trajectory Optimization
- Gipuma + Fusibile - 3.0 |
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Commercial Software
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Viewpoints and Trajectory Optimization
- DJI Terra
- MetaShape
- Pix4Dmapper
- RealityCapture - US/) |
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