D. Scaramuzza.
Awesome 3D reconstruction list
A curated list of papers & resources linked to 3D reconstruction from images.
This page lists names, links and short descriptions. The original list on GitHub is the source and belongs to its authors.
SLAM Tutorial & survey
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.
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
Computer vision books
Computer Vision: Algorithms and Applications
. R. Szeliski. 2010.
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.
SfM papers >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.
SfM papers >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.
SfM papers >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.
SfM papers >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.
SfM papers >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.
SfM papers >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.
SfM papers >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
Multiple View Stereovision >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.
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.
Multiple View Stereovision >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 Stereovision >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.
Multiple View Stereovision >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 SfM (Structure from Motion)
Bundler
GNU General Public License - contamination
Colmap
BSD 3-clause license - Permissive
TeleSculptor
BSD 3-Clause license - Permissive
MicMac
CeCILL-B
MVE
BSD 3-Clause license + parts under the GPL 3 license
OpenMVG
MPL2 - Permissive
OpenSfM
Python
TheiaSfM
New BSD license - Permissive
OpenSource Multiple View Geometry Library Solvers
OpenGV
BSD - permissive
OpenSource MVS (Multiple View Stereovision)
Colmap
BSD 3-clause license - Permissive
GPUIma + fusibile
C++ CUDA
HPMVS
GNU General Public License - contamination
MICMAC
CeCILL-B
MVE
BSD 3-Clause license + parts under the GPL 3 license
OpenMVS
C++ (CUDA optional)
PMVS
C++ CUDA
SMVS Shading-aware Multi-view Stereo
BSD-3-Clause license
OpenSource SLAM (Simultaneous Localization And Mapping)
COSLAM
GNU General Public License
DTSLAM-Deferred Triangulation SLAM
modified BSD
LSD-SLAM
C++/ROS
MAPLAB-ROVIOLI
C++/ROS
ORB-SLAM
GPLv3
REBVO - Realtime Edge Based Visual Odometry for a Monocular Camera
GNU General Public License
Large scale image retrieval / CBIR (Content Based Image Retrieval)
OpenSource minimization
CERES SOLVER
BSD License
GTSAM
BSD License
G2O
BSD License + L/GPL3 restriction
NLOPT
LGPL
Nearest Neighbor Search
Mesh storage processing
Features detection/Description
From handcrafted to deep local features
. G. Csurka, C. R. Dance, M. Humenberger. 2018.
AKAZE
MSURF/MLDB
DART
x
KAZE
MSURF/MLDB
Features detection/Description >"Real time" oriented methods
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
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.
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