krahets/awesome-mvs
krahets/awesome-mvs là dự án GitHub với 279 sao trong nhóm AI Tools. A curated list of tutorials, papers, software related to multi-view stereo.
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Awesome MVS 
A curated list of tutorials, papers, software related to multi-view stereo.
Please visit awesome-computer-vision for a more generic computer vision list.
More multi-view stereo papers is available on Awesome-Learning-MVS, Awesome-PatchMatch-MVS, Awesome-MVS.
Feel free to create a Pull Request to add new papers!
Table of Contents
- Survey
- Paper
- Multi-View Stereo
- Depth-Map Representation
- Volumetric Representation
- Neural Implicit Representation
- Mesh Texturing
- Viewpoints and Trajectory Optimization
- Multi-View Stereo
- Benchmark
- Open Source
- Commercial Software
- License
Tutorial
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Multi-View Stereo: A Tutorial. Y. Furukawa, C. Hernández. Foundations and Trends® in Computer Graphics and Vision, 2015.
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Multiple view geometry in computer vision. Hartley, Richard, and Andrew Zisserman. Cambridge university press, 2003.
Survey
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A comparison and evaluation of multi-view stereo reconstruction algorithms. Seitz, Steven M., et al. CVPR 2006.
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On benchmarking camera calibration and multi-view stereo for high resolution imagery. Strecha, Christoph, et al. CVPR 2008.
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State of the art in high density image matching. F. Remondino, M.G. Spera, E. Nocerino, F. Menna, F. Nex . The Photogrammetric Record 2014.
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Deep Learning for Multi-View Stereo via Plane Sweep: A Survey. Zhu, Qingtian, et al. arXiv 2021.
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Multi-view stereo in the Deep Learning Era: A comprehensive review. Wang, Xiang, et al. Displays 2021.
Paper
Multi-View Stereo
Depth-Map Representation
Geometry-Based Methods
- Multi-view stereo revisited. Goesele, Michael, Brian Curless, and Steven M. Seitz. CVPR 2006.
- Multi-view stereo for community photo collections. Goesele, Michael, et al. ICCV 2007.
- Using multiple hypotheses to improve depth-maps for multi-view stereo. Campbell, Neill DF, et al. ECCV 2008.
- Towards high-resolution large-scale multi-view stereo. Hiep, Vu Hoang, et al. CVPR 2009.
- Towards internet-scale multi-view stereo. Furukawa, Yasutaka, et al. CVPR 2010.
- (PMVS) Accurate, Dense, and Robust Multiview Stereopsis. Y. Furukawa, J. Ponce. CVPR 2007. PAMI 2010.
- Multi-view reconstruction preserving weakly-supported surfaces. Jancosek, Michal, and Tomás Pajdla. CVPR 2011.
- Efficient large-scale multi-view stereo for ultra high-resolution image sets. Tola, Engin, Christoph Strecha, and Pascal Fua. MVA 2012.
- (Similar to OpenMVS) Accurate multiple view 3d reconstruction using patch-based stereo for large-scale scenes. Shen, Shuhan. TIP 2013.
- MVE-A Multi-View Reconstruction Environment. Fuhrmann, Simon, Fabian Langguth, and Michael Goesele. GCH 2014.
- Patchmatch based joint view selection and depthmap estimation. Zheng, Enliang, et al. CVPR 2014.
- (Gipuma) Massively parallel multiview stereopsis by surface normal diffusion. Galliani, Silvano, Katrin Lasinger, and Konrad Schindler. ICCV 2015.
- (Colmap) Pixelwise View Selection for Unstructured Multi-View Stereo. J. L. Schönberger, E. Zheng, M. Pollefeys, J.-M. Frahm. ECCV 2016.
- TAPA-MVS: Textureless-aware patchmatch multi-view stereo. Romanoni, Andrea, and Matteo Matteucci. ICCV 2019.
- Pyramid multi‐view stereo with local consistency. Liao, Jie, et al. Computer Graphics Forum 2019.
- (ACMM) Multi-scale geometric consistency guided multi-view stereo. Qingshan Xu, and Wenbing Tao. CVPR 2019.
- (ACMP) Planar Prior Assisted PatchMatch Multi-View Stereo. Qingshan Xu, and Wenbing Tao. AAAI 2020.
- DP-MVS: Detail Preserving Multi-View Surface Reconstruction of Large-Scale Scenes. Liyang Zhou, et al. Remote Sens. 2021.
Surpervised Learning
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MVSNet: Depth Inference for Unstructured Multi-view Stereo, Y. Yao, Z. Luo, S. Li, T. Fang, L. Quan. ECCV 2018.
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DeepMVS: Learning Multi-View Stereopsis, Huang, P. and Matzen, K. and Kopf, J. and Ahuja, N. and Huang, J. CVPR 2018.
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MVDepthNet: Real-time multiview depth estimation neural network. Wang, Kaixuan, and Shaojie Shen. 3DV 2018.
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Recurrent MVSNet for high-resolution multi-view stereo depth inference. Yao, Yao, et al. CVPR 2019.
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DPSNet: End-to-end deep plane sweep stereo. Im, Sunghoon, et al. arXiv 2019.
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P-MVSNet: Learning patch-wise matching confidence aggregation for multi-view stereo. Luo, Keyang, et al. ICCV 2019.
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(PointMVSNet) Point-based Multi-view Stereo Network, Rui Chen, Songfang Han, Jing Xu, Hao Su. ICCV 2019.
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Pyramid multi-view stereo net with self-adaptive view aggregation. Yi, Hongwei, et al. ECCV 2020.
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(CasMVSNet) Cascade cost volume for high-resolution multi-view stereo and stereo matching. Gu, Xiaodong, et al. CVPR 2020.
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(CVP-MVSNet) Cost volume pyramid based depth inference for multi-view stereo. Yang, Jiayu, et al. CVPR 2020.
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Fast-MVSNet: Sparse-to-dense multi-view stereo with learned propagation and gauss-newton refinement. Yu, Zehao, and Shenghua Gao. CVPR 2020.
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(AttMVS) Attention-aware multi-view stereo. Luo, Keyang, et al. CVPR 2020.
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(Vis-MVSNet) Visibility-aware multi-view stereo network. Zhang, Jingyang, et al. BMVC 2020.
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Visibility-aware point-based multi-view stereo network. Chen, Rui, et al. PAMI 2020.
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PVSNet: Pixelwise visibility-aware multi-view stereo network. Xu, Qingshan, and Wenbing Tao. arXiv 2020.
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BP-MVSNet: Belief-propagation-layers for multi-view-stereo. Sormann, Christian, et al. 3DV 2020.
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DeepC-MVS: Deep confidence prediction for multi-view stereo reconstruction. Kuhn, Andreas, et al. 3DV 2020.
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Mesh-guided multi-view stereo with pyramid architecture. Wang, Yuesong, et al. CVPR 2020.
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PatchmatchNet: Learned multi-view patchmatch stereo. Wang, Fangjinhua, et al. CVPR 2021.
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AA-RMVSNet: Adaptive aggregation recurrent multi-view stereo network. Wei, Zizhuang, et al. ICCV 2021.
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PatchMatch-RL: Deep MVS with Pixelwise Depth, Normal, and Visibility. Lee, Jae Yong, et al. ICCV 2021.
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EPP-MVSNet: Epipolar-Assembling Based Depth Prediction for Multi-View Stereo. Ma, Xinjun, et al. ICCV 2021.
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Deep multi-view stereo gone wild. Darmon, François, et al. 3DV 2021.
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(GBiNet) Generalized Binary Search Network for Highly-Efficient Multi-View Stereo. Zhenxing Mi, Di Chang, Dan Xu. CVPR 2022.
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(UniMVSNet) Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation. Rui Peng, et al. CVPR 2022.
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MVSTER: Epipolar Transformer for Efficient Multi-View Stereo. Xiaofen Wang, et al. ECCV 2022.
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TransMVSNet: Global Context-aware Multi-view Stereo Network with Transformers. Yikang Ding, et al. CVPR 2022.
Unsurpervised Learning
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Learning unsupervised multi-view stereopsis via robust photometric consistency. Khot, Tejas, et al. arXiv 2019.
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MVS2: Deep unsupervised multi-view stereo with multi-view symmetry. Dai, Yuchao, et al. 3DV 2019.
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Mˆ3VSNet: Unsupervised multi-metric multi-view stereo network. Huang, Baichuan, et al. ICIP 2021.
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Self-supervised multi-view stereo via effective co-segmentation and data-augmentation. Xu, Hongbin, et al. AAAI 2021.
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Self-supervised Learning of Depth Inference for Multi-view Stereo. Yang, Jiayu, Jose M. Alvarez, and Miaomiao Liu. CVPR 2021.
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Digging into Uncertainty in Self-supervised Multi-view Stereo. Xu, Hongbin, et al. ICCV 2021.
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RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering. Di Chang, et al. arXiv 2022.
Volumetric Representation
Geometry-Based Methods
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(VRIP) A volumetric method for building complex models from range images. Curless, Brian, and Marc Levoy. PACMCGIT 1996.
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Reliable surface reconstruction from multiple range images. Hilton, Adrian, et al. ECCV 1996.
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Consensus surfaces for modeling 3D objects from multiple range images. Wheeler, Mark D., Yoichi Sato, and Katsushi Ikeuchi. ICCV 1998.
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A Theory of Shape by Space Carving. Kiriakos N. Kutulakos, S. Seitz. IJCV 2000.
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KinectFusion: Real-time dense surface mapping and tracking. Richard A. Newcombe, S. Izadi, Otmar Hilliges, D. Molyneaux, David Kim, A. Davison, P. Kohli, J. Shotton, Steve Hodges, A. Fitzgibbon. ISMAR 2011.
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(VoxelHashing) Real-time 3D reconstruction at scale using voxel hashing. M. Nießner, M. Zollhöfer, S. Izadi, M. Stamminger. ACM Trans. Graph 2013.
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ElasticFusion: Dense SLAM Without A Pose Graph. Thomas Whelan, et al. Robotics: Science and Systems 2015.
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BundleFusion: real-time globally consistent 3D reconstruction using on-the-fly surface re-integration. Angela Dai, M. Nießner, M. Zollhöfer, S. Izadi, C. Theobalt. TOGS 2016.
Learning-Based Methods
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Learning a multi-view stereo machine. Kar, Abhishek, Christian Häne, and Jitendra Malik. NeurIPS 2017.
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SurfaceNet: An end-to-end 3d neural network for multiview stereopsis. Ji, Mengqi, et al. ICCV 2017.
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RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials, D. Paschalidou and A. O. Ulusoy and C. Schmitt and L. Gool and A. Geiger. CVPR 2018.
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Atlas: End-to-end 3d scene reconstruction from posed images. Murez, Zak, et al. ECCV 2020.
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RoutedFusion: Learning real-time depth map fusion. Weder, Silvan, et al. CVPR 2020.
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SurfaceNet+: An end-to-end 3D neural network for very sparse multi-view stereopsis. Ji, Mengqi, et al. PAMI 2020.
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NeuralRecon: Real-time coherent 3D reconstruction from monocular video. Sun, Jiaming, et al. CVPR 2021.
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NeuralFusion: Online depth fusion in latent space. Weder, Silvan, et al. CVPR 2021.
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PlanarRecon: Real-time 3D Plane Detection and Reconstruction from Posed Monocular Videos. Yuming Xie. CVPR 2022.
Neural Implicit Representation
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(DVR) Differentiable Volumetric Rendering: Learning implicit 3d representations without 3d supervision. Niemeyer, Michael, et al. CVPR 2020.
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(IDR) Multiview neural surface reconstruction by disentangling geometry and appearance. Yariv, Lior, et al. NeurIPS 2020.
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UNISURF: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction. Oechsle, Michael, Songyou Peng, and Andreas Geiger. ICCV 2021.
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NeuS: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. Wang, Peng, et al. NeurIPS 2021.
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(VolSDF) Volume rendering of neural implicit surfaces. Yariv, Lior, et al. NeurIPS 2021.
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NerfingMVS: Guided optimization of neural radiance fields for indoor multi-view stereo. Wei, Yi, et al. ICCV 2021.
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(ManhattanSDF) Neural 3D Scene Reconstruction with the Manhattan-world Assumption. Haoyu Guo, et al. CVPR 2022.
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(NeuralRecon-W) Neural 3D Reconstruction in the Wild. Jiaming Sun, et al. SIGGRAPH 2022.
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Neural RGB-D Surface Reconstruction. Dejan Azinović, et al. CVPR 2022.
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SurRF: Unsupervised Multi-view Stereopsis by Learning Surface Radiance Field Jinzhi Zhang, et al. TPAMI 2022.
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MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction. Zehao Yu, et al. arXiv 2022.
Mesh Texturing
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Seamless image-based texture atlases using multi-band blending. C. Allène, J-P. Pons and R. Keriven. ICPR 2008.
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(mvs-texturing) Let There Be Color! - Large-Scale Texturing of 3D Reconstructions. M. Waechter, N. Moehrle, M. Goesele. ECCV 2014.
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Texture Mapping for 3D Reconstruction with RGB-D Sensor. Yanping Fu, Qingan Yan, Long Yang, Jie Liao, Chunxia Xiao. CVPR 2018.
Viewpoints and Trajectory Optimization
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Next Best View Planning for Active Model Improvement. Dunn, Enrique, and Jan-Michael Frahm. BMVC 2009.
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Receding Horizon "Next-Best-View" Planner for 3D Exploration. Andreas Bircher, et al. ICRA 2016.
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Submodular Trajectory Optimization for Aerial 3D Scanning. M. Roberts, A. Truong, D. Dey, S. Sinha, A. Kapoor, N. Joshi, P. Hanrahan. 2017.
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Aerial path planning for urban scene reconstruction: A continuous optimization method and benchmark. Smith, Neil, et al. 2018.
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Plan3D: Viewpoint and trajectory optimization for aerial multi-view stereo reconstruction. Hepp, Benjamin, Matthias Nießner, and Otmar Hilliges. ACM TOG 2018.
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Learn-to-Score: Efficient 3D Scene Exploration by Predicting View Utility. Benjamin Hepp, et al. ECCV 2018.
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Automatic and semantically-aware 3D UAV flight planning for image-based 3D reconstruction. Koch, Tobias, Marco Körner, and Friedrich Fraundorfer. Remote Sensing 2019.
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Next-Best View Policy for 3D Reconstruction. Daryl Peralta, et al. ECCV Workshop 2020.
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Offsite aerial path planning for efficient urban scene reconstruction. Zhou, Xiaohui, et al. ACM TOG 2020.
Benchmark
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DTU. Large scale multi-view stereopsis evaluation. Jensen, Rasmus, et al. CVPR 2014. Large-scale data for multiple-view stereopsis. Aanæs, Henrik, et al. ICCV2016.
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Tanks and Temples: Benchmarking large-scale scene reconstruction. Knapitsch, Arno, et al. ACM TOG 2017.
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ETH3D. A multi-view stereo benchmark with high-resolution images and multi-camera videos. Schops, Thomas, et al. CVPR 2017.
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ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes. Angela Dai, Angel X. Chang, M. Savva, Maciej Halber, T. Funkhouser, M. Nießner. CVPR 2017.
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BlendedMVS: A large-scale dataset for generalized multi-view stereo networks. Yao, Yao, et al. CVPR 2020.
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GigaMVS: A Benchmark for Ultra-large-scale Gigapixel-level 3D Reconstruction. Zhang, Jianing, et al. PAMI 2021.
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Multi-sensor large-scale dataset for multi-view 3D reconstruction. Oleg Voynov, et al. arXiv 2022.
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Capturing, Reconstructing, and Simulating: the UrbanScene3D Dataset. Liqiang Lin, et al. ECCV 2022.
Open Source
| Project | Language | License |
|---|---|---|
| CMVS-PMVS | C++, CUDA | GPL |
| Colmap | C++, CUDA | BSD 3-Clause |
| Gipuma + Fusibile | C++, CUDA | GPL-3.0 |
| MeshRoom(AliceVision) | C++, Python | MPL2 |
| MVE | C++ | BSD 3-Clause |
| OpenMVS | C++, CUDA(Optional) | AGPL3 |
| MVS-Texturing | C++ | BSD 3-Clause |
Commercial Software
| Software | Company |
|---|---|
| ContextCapture | Bentley Systems |
| DJI Terra | DJI |
| MetaShape | Agisoft |
| Pix4Dmapper | Pix4D |
| RealityCapture | Epic Games |
License
MIT
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