Awesome 3D reconstruction list

A curated list of papers & resources linked to 3D reconstruction from images.

Note that:

• This list is not exhaustive,
• Tables use alphabetical order for fairness.

If you look to a more generic computer vision awesome list please check this list

Contents

• Tutorials

• Papers

  • SLAM

  • SFM

    • Incremental SfM
    • Global SfM
    • Hierarchical SfM
    • Multi-Stage SfM
    • Non Rigid SfM
      
      
    • Viewing graph optimization
    • Unordered feature tracking
    • Large scale image matching for SfM

  • Localization

    • Real time localization in SfM reconstructions

  • MVS

    • Point cloud computation
    • Surface computation & refinements
    • Machine Learning MVS
    • Multiple View Mesh Texturing

  • UAV Trajectory Optimization for model completeness

• OpenSource software resources

  • SfM
  • Multiple View Geometry Library Solvers
  • MVS (Multiple View Stereovision)
  • SLAM
  • Large Scale Image Retrieval
  • Minimization
  • Nearest Neighbor Search
  • Mesh storage processing

• Feature detection description

• Datasets with ground truth - Reproducible research

• License

• Contributing

Tutorials

SLAM Tutorial & survey

Micro Flying Robots: from Active Vision to Event-based Vision D. Scaramuzza.

ICRA 2016 Aerial Robotics - (Visual odometry) D. Scaramuzza

Simultaneous Localization And Mapping: Present, Future, and the Robust-Perception Age. C. Cadena, L. Carlone, H. Carrillo, Y. Latif, D. Scaramuzza, J. Neira, I. D. Reid, J. J. Leonard.

• "The paper summarizes the outcome of the workshop “The Problem of Mobile Sensors: Setting future goals and indicators of progress for SLAM” held during the Robotics: Science and System (RSS) conference (Rome, July 2015)."

Visual Odometry: Part I - The First 30 Years and Fundamentals, D. Scaramuzza and F. Fraundorfer, IEEE Robotics and Automation Magazine, Volume 18, issue 4, 2011

Visual Odometry: Part II - Matching, robustness, optimization, and applications, F. Fraundorfer and D. Scaramuzza, IEEE Robotics and Automation Magazine, Volume 19, issue 2, 2012

Large-scale, real-time visual-inertial localization revisited S. Lynen, B. Zeisl, D. Aiger, M. Bosse, J. Hesch, M. Pollefeys, R. Siegwart and T. Sattler. Arxiv 2019.

SfM tutorial

Open Source Structure-from-Motion. M. Leotta, S. Agarwal, F. Dellaert, P. Moulon, V. Rabaud. CVPR 2015 Tutorial (material).

Large-scale 3D Reconstruction from Images. T. Shen, J. Wang, T.Fang, L. Quan. ACCV 2016 Tutorial.

MVS tutorial

Multi-View Stereo: A Tutorial. Y. Furukawa, C. Hernández. Foundations and Trends® in Computer Graphics and Vision, 2015.

State of the Art 3D Reconstruction Techniques N. Snavely, Y. Furukawa, CVPR 2014 tutorial slides. Introduction MVS with priors - Large scale MVS

RGB-D mapping

3D indoor scene modeling from RGB-D data: a survey K. Chen, YK. Lai and SM. Hu. Computational Visual Media 2015.

State of the Art on 3D Reconstruction with RGB-D Cameras K. Hildebrandt and C. Theobalt EUROGRAPHICS 2018.

All in one tutorial

Introduction of Visual SLAM, Structure from Motion and Multiple View Stereo. Yu Huang 2014.

Computer vision books

Computer Vision: Algorithms and Applications. R. Szeliski. 2010.

Papers

SLAM/VO

Visual odometry (image based only)

Real-time simultaneous localisation and mapping with a single camera. A. J. Davison. ICCV 2003.

Visual odometry. D. Nister, O. Naroditsky, and J. Bergen. CVPR 2004.

Real time localization and 3d reconstruction. E. Mouragnon, M. Lhuillier, M. Dhome, F. Dekeyser, and P. Sayd. CVPR 2006.

Parallel Tracking and Mapping for Small AR Workspaces. G. Klein, D. Murray. ISMAR 2007.

Real-Time 6-DOF Monocular Visual SLAM in a Large-scale Environments. H. Lim, J. Lim, H. Jin Kim. ICRA 2014.

Direct Sparse Odometry, J. Engel, V. Koltun, D. Cremers, arXiv:1607.02565, 2016.

Visual SLAM algorithms: a survey from 2010 to 2016, T. Taketomi, H. Uchiyama, S. Ikeda, IPSJ T Comput Vis Appl 2017.

∇SLAM: Dense SLAM meets Automatic Differentiation. K. M. Jatavallabhula, G. Iyer, L. Paull. arXiv:1910.10672, 2019.

Direct Sparse Mapping J. Zubizarreta, I. Aguinaga and J. M. M. Montiel. arXiv:1904.06577, 2019.

OpenVSLAM: A Versatile Visual SLAM Framework Sumikura, Shinya and Shibuya, Mikiya and Sakurada, Ken. In Proceedings of the 27th ACM International Conference on Multimedia 2019

SfM papers

Incremental SfM

Photo Tourism: Exploring Photo Collections in 3D. N. Snavely, S. M. Seitz, and R. Szeliski. SIGGRAPH 2006.

Towards linear-time incremental structure from motion. C. Wu. 3DV 2013.

Structure-from-Motion Revisited. Schöenberger, Frahm. CVPR 2016.

Global SfM

Combining two-view constraints for motion estimation V. M. Govindu. CVPR, 2001.

Lie-algebraic averaging for globally consistent motion estimation. V. M. Govindu. CVPR, 2004.

Robust rotation and translation estimation in multiview reconstruction. D. Martinec and T. Pajdla. CVPR, 2007.

Non-sequential structure from motion. O. Enqvist, F. Kahl, and C. Olsson. ICCV OMNIVIS Workshops 2011.

Global motion estimation from point matches. M. Arie-Nachimson, S. Z. Kovalsky, I. KemelmacherShlizerman, A. Singer, and R. Basri. 3DIMPVT 2012.

Global Fusion of Relative Motions for Robust, Accurate and Scalable Structure from Motion. P. Moulon, P. Monasse and R. Marlet. ICCV 2013.

A Global Linear Method for Camera Pose Registration. N. Jiang, Z. Cui, P. Tan. ICCV 2013.

Global Structure-from-Motion by Similarity Averaging. Z. Cui, P. Tan. ICCV 2015.

Linear Global Translation Estimation from Feature Tracks Z. Cui, N. Jiang, C. Tang, P. Tan, BMVC 2015.

Hierarchical SfM

Structure-and-Motion Pipeline on a Hierarchical Cluster Tree. A. M.Farenzena, A.Fusiello, R. Gherardi. Workshop on 3-D Digital Imaging and Modeling, 2009.

Randomized Structure from Motion Based on Atomic 3D Models from Camera Triplets. M. Havlena, A. Torii, J. Knopp, and T. Pajdla. CVPR 2009.

Efficient Structure from Motion by Graph Optimization. M. Havlena, A. Torii, and T. Pajdla. ECCV 2010.

Hierarchical structure-and-motion recovery from uncalibrated images. Toldo, R., Gherardi, R., Farenzena, M. and Fusiello, A.. CVIU 2015.

Multi-Stage SfM

Parallel Structure from Motion from Local Increment to Global Averaging. S. Zhu, T. Shen, L. Zhou, R. Zhang, J. Wang, T. Fang, L. Quan. arXiv 2017.

Multistage SFM : Revisiting Incremental Structure from Motion. R. Shah, A. Deshpande, P. J. Narayanan. 3DV 2014. -> Multistage SFM: A Coarse-to-Fine Approach for 3D Reconstruction, arXiv 2016.

HSfM: Hybrid Structure-from-Motion. H. Cui, X. Gao, S. Shen and Z. Hu, ICCV 2017.

Non Rigid SfM

Robust Structure from Motion in the Presence of Outliers and Missing Data. G. Wang, J. S. Zelek, J. Wu, R. Bajcsy. 2016.

Viewing graph optimization

Skeletal graphs for efficient structure from motion. N. Snavely, S. Seitz, R. Szeliski. CVPR 2008

Optimizing the Viewing Graph for Structure-from-Motion. C. Sweeney, T. Sattler, M. Turk, T. Hollerer, M. Pollefeys. ICCV 2015

Graph-Based Consistent Matching for Structure-from-Motion. T. Shen, S. Zhu, T. Fang, R. Zhang, L. Quan. ECCV 2016.

Unordered feature tracking

Unordered feature tracking made fast and easy. P. Moulon and P. Monasse. CVMP 2012.

Point Track Creation in Unordered Image Collections Using Gomory-Hu Trees. Svärm, Simayijiang, Enqvist, Olsson. ICPR 2012.

Fast connected components computation in large graphs by vertex pruning. A. Lulli, E. Carlini, P. Dazzi, C. Lucchese, and L. Ricci. IEEE Transactions on Parallel and Distributed Systems 2016.

Large scale image matching for SfM

Video Google: A Text Retrieval Approach to Object Matching in Video. J. Sivic, F. Schaffalitzky and A. Zisserman. ICCV 2003.

Scalable Recognition with a Vocabulary Tree. Nister, Stewenius, CVPR 2006.

Building Rome in a Day. S. Agarwal, N. Snavely, I. Simon, S. M. Seitz, R. Szeliski. ICCV 2009.

Product quantization for nearest neighbor search. H. Jégou, M. Douze and C. Schmid. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011.

Fast and Accurate Image Matching with Cascade Hashing for 3D Reconstruction. J. Cheng, C. Leng, J. Wu, H. Cui, H. Lu. CVPR 2014.

Recent developments in large-scale tie-point matching. Hartmann, Havlena, Schindler. ISPRS 2016.

Graphmatch: Efficient Large-Scale Graph Construction for Structure from Motion. C. Qiaodong, V. Fragoso, C. Sweeney and P. Sen. 3DV 2017.

Localization

Real time localization in SfM reconstructions

Real-time Image-based 6-DOF Localization in Large-Scale Environments. Lim, Sinha, Cohen, Uyttendaele. CVPR 2012.

Get Out of My Lab: Large-scale, Real-Time Visual-Inertial Localization. Lynen, Sattler, Bosse, Hesch, Pollefeys, Siegwart. RSS 2015.

DSAC - Differentiable RANSAC for Camera Localization. E. Brachmann, A. Krull, S. Nowozin, J. Shotton, F. Michel, S. Gumhold, C. Rother. CVPR 2017.

Learning Less is More - 6D Camera Localization via 3D Surface Regression. E. Brachmann, C. Rother. Submitted to CVPR 2018.

Multiple View Stereovision

Point cloud computation

Accurate, Dense, and Robust Multiview Stereopsis. Y. Furukawa, J. Ponce. CVPR 2007. PAMI 2010

State of the art in high density image matching. F. Remondino, M.G. Spera, E. Nocerino, F. Menna, F. Nex . The Photogrammetric Record 29(146), 2014.

Progressive prioritized multi-view stereo. A. Locher, M. Perdoch and L. Van Gool. CVPR 2016.

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. A. Romanoni, M. Matteucci. ICCV 2019

Surface computation & refinements

Efficient Multi-View Reconstruction of Large-Scale Scenes using Interest Points, Delaunay Triangulation and Graph Cuts. P. Labatut, J-P. Pons, R. Keriven. ICCV 2007

Multi-View Stereo via Graph Cuts on the Dual of an Adaptive Tetrahedral Mesh. S. N. Sinha, P. Mordohai and M. Pollefeys. ICCV 2007.

Towards high-resolution large-scale multi-view stereo. H.-H. Vu, P. Labatut, J.-P. Pons, R. Keriven. CVPR 2009.

Refinement of Surface Mesh for Accurate Multi-View Reconstruction. R. Tylecek and R. Sara. IJVR 2010.

High Accuracy and Visibility-Consistent Dense Multiview Stereo. H.-H. Vu, P. Labatut, J.-P. Pons, R. Keriven. Pami 2012.

Exploiting Visibility Information in Surface Reconstruction to Preserve Weakly Supported Surfaces M. Jancosek et al. 2014.

Floating Scale Surface Reconstruction S. Fuhrmann and M. Goesele. SIGGRAPH 2014

A New Variational Framework for Multiview Surface Reconstruction. B. Semerjian. ECCV 2014.

Photometric Bundle Adjustment for Dense Multi-View 3D Modeling. A. Delaunoy, M. Pollefeys. CVPR2014.

Global, Dense Multiscale Reconstruction for a Billion Points. B. Ummenhofer, T. Brox. ICCV 2015.

Efficient Multi-view Surface Refinement with Adaptive Resolution Control. S. Li, S. Yu Siu, T. Fang, L. Quan. ECCV 2016.

Multi-View Inverse Rendering under Arbitrary Illumination and Albedo, K. Kim, A. Torii, M. Okutomi, ECCV2016.

Shading-aware Multi-view Stereo, F. Langguth and K. Sunkavalli and S. Hadap and M. Goesele, ECCV 2016.

Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity, C. Mostegel, R. Prettenthaler, F. Fraundorfer and H. Bischof. CVPR 2017.

Multi-View Stereo with Single-View Semantic Mesh Refinement, A. Romanoni, M. Ciccone, F. Visin, M. Matteucci. ICCVW 2017

Out-of-Core Surface Reconstruction via Global T GV Minimization N. Poliarnyi. Agisoft. 2021.

Machine Learning based MVS

Matchnet: Unifying feature and metric learning for patch-based matching, X. Han, Thomas Leung, Y. Jia, R. Sukthankar, A. C. Berg. CVPR 2015.

Stereo matching by training a convolutional neural network to compare image patches, J., Zbontar, and Y. LeCun. JMLR 2016.

Efficient deep learning for stereo matching, W. Luo, A. G. Schwing, R. Urtasun. CVPR 2016.

Learning a multi-view stereo machine, A. Kar, C. Häne, J. Malik. NIPS 2017.

Learned multi-patch similarity, W. Hartmann, S. Galliani, M. Havlena, L. V. Gool, K. Schindler.I CCV 2017.

Surfacenet: An end-to-end 3d neural network for multiview stereopsis, Ji, M., Gall, J., Zheng, H., Liu, Y., Fang, L. ICCV2017.

DeepMVS: Learning Multi-View Stereopsis, Huang, P. and Matzen, K. and Kopf, J. and Ahuja, N. and Huang, J. CVPR 2018.

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.

MVSNet: Depth Inference for Unstructured Multi-view Stereo, Y. Yao, Z. Luo, S. Li, T. Fang, L. Quan. ECCV 2018.

Learning Unsupervised Multi-View Stereopsis via Robust Photometric Consistency, T. Khot, S. Agrawal, S. Tulsiani, C. Mertz, S. Lucey, M. Hebert. 2019.

DPSNET: END-TO-END DEEP PLANE SWEEP STEREO, Sunghoon Im, Hae-Gon Jeon, Stephen Lin, In So Kweon. 2019.

Point-based Multi-view Stereo Network, Rui Chen, Songfang Han, Jing Xu, Hao Su. ICCV 2019.

UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction M. Oechsle, S. Peng, and A. Geiger. ICCV 2021.

Multiple View Mesh Texturing

Seamless image-based texture atlases using multi-band blending. C. Allène, J-P. Pons and R. Keriven. ICPR 2008.

Let There Be Color! - Large-Scale Texturing of 3D Reconstructions. M. Waechter, N. Moehrle, M. Goesele. ECCV 2014.

UAV Trajectory Optimization for model completeness

Submodular Trajectory Optimization for Aerial 3D Scanning. M. Roberts, A. Truong, D. Dey, S. Sinha, A. Kapoor, N. Joshi, P. Hanrahan. 2017.

OpenSource resources

OpenSource SfM (Structure from Motion)

OpenSource Multiple View Geometry Library Solvers

OpenSource MVS (Multiple View Stereovision)

OpenSource SLAM (Simultaneous Localization And Mapping)

Large scale image retrieval / CBIR (Content Based Image Retrieval)

OpenSource minimization

Nearest Neighbor Search

Mesh storage processing

Features

Features detection/Description

From handcrafted to deep local features. G. Csurka, C. R. Dance, M. Humenberger. 2018.

"Real time" oriented methods

Datasets with ground truth - Reproducible research

Feature detection/description repeatability

VGG Oxford 8 dataset with GT homographies + matlab code.

Hannover - Region Detector Evaluation Data Set Similar to the previous (5 dataset). Datasets have multiple image resolution & an increased GT homographies precision.

DTU - Robot Image Data Sets - Point Feature Data Set 60 scenes with know calibration & different illuminations.

Corresponding interest point patches for descriptor learning

Corresponding patches, saved with a canonical scale and orientation.

Multi-view Stereo Correspondence Dataset

HPatches Dataset linked to the ECCV16 workshop "Local Features: State of the art, open problems and performance evaluation"

Monocular odometry dataset

Mono dataset 50 real-world sequences. Dataset linked to the DSO Visual Odometry paper.

MVS - Point Cloud - Surface accuracy

Middlebury Multi-view Stereo See "A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms". CVPR 2006.

Dense MVS See "On Benchmarking Camera Calibration and Multi-View Stereo for High Resolution Imagery". CVPR 2008.

DTU - Robot Image Data Sets -MVS Data Set See “Large Scale Multi-view Stereopsis Evaluation“. CVPR 2014.

A Multi-View Stereo Benchmark with High-Resolution Images and Multi-Camera Videos in Unstructured Scenes, T. Schöps, J. L. Schönberger, S. Galiani, T. Sattler, K. Schindler, M. Pollefeys, A. Geiger,. CVPR 2017.

Tanks and Temples: Benchmarking Large-Scale Scene Reconstruction, A. Knapitsch, J. Park, Q.Y. Zhou and V. Koltun. SIGGRAPH 2017.

License

License

To the extent possible under law, Pierre Moulon has waived all copyright and related or neighboring rights to this work.

Contributing

Please see CONTRIBUTING for details.