The DOI matches its Crossref record.
Multiple View Geometry in Computer Vision
Abstract
A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective geometry and photogrammetry. Here, the authors cover the geometric principles and their algebraic representation in terms of camera projection matrices, the fundamental matrix and the trifocal tensor. The theory and methods of computation of these entities are discussed with real examples, as is their use in the reconstruction of scenes from multiple images. The new edition features an extended introduction covering the key ideas in the book (which itself has been updated with additional examples and appendices) and significant new results which have appeared since the first edition. Comprehensive background material is provided, so readers familiar with linear algebra and basic numerical methods can understand the projective geometry and estimation algorithms presented, and implement the algorithms directly from the book.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Accurate real-time pose estimation without iterative refinement would be required to power self-driving image-guidance systems [45].”
From Rapid patient-specific neural networks for intraoperative X-ray to volume registration (2025) · cites this paper as [45]
In the citing paper
“Within the calibration process the internal parameters of each camera as well as the relative orientation among the cameras were determined [20].”
From Diving-Flight Aerodynamics of a Peregrine Falcon (Falco peregrinus) (2014) · cites this paper as [20]
Citations
Cited by
Sorted by citations
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
Scharstein, D., & Szeliski, R. (2002) · International Journal of Computer Vision · doi:10.1023/a:1014573219977 · Cited by 6,740
ORB-SLAM: A Versatile and Accurate Monocular SLAM System
Mur-Artal, R., Montiel, J. M. M., & Tardos, J. D. (2015) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2463671 · Cited by 6,778
A performance evaluation of local descriptors
Mikolajczyk, K., & Schmid, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.188 · Cited by 6,723
Unscented Filtering and Nonlinear Estimation
Julier, S., & Uhlmann, J. K. (2004) · Proceedings of the IEEE · doi:10.1109/jproc.2003.823141 · Cited by 6,798
Structure-from-Motion Revisited
Schonberger, J. L., & Frahm, J.-M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.445 · Cited by 6,433
VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
Qin, T., Li, P., & Shen, S. (2018) · IEEE Transactions on Robotics · doi:10.1109/tro.2018.2853729 · Cited by 4,669
Parallel Tracking and Mapping for Small AR Workspaces
Klein, G., & Murray, D. (2007) · 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2007.4538852 · Cited by 4,346
Bundle Adjustment — A Modern Synthesis
Triggs, B., McLauchlan, P. F., Hartley, R. I., & Fitzgibbon, A. W. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-44480-7_21 · Cited by 3,988
Self-citationHuman3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments
Ionescu, C., Papava, D., Olaru, V., & Sminchisescu, C. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.248 · Cited by 3,653
Robust wide-baseline stereo from maximally stable extremal regions
Matas, J., Chum, O., Urban, M., & Pajdla, T. (2004) · Image and Vision Computing · doi:10.1016/j.imavis.2004.02.006 · Cited by 3,760
3D Object Representations for Fine-Grained Categorization
Krause, J., Stark, M. M., Deng, J., & Fei-Fei, L. (2013) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2013.77 · Cited by 3,666
Scale & Affine Invariant Interest Point Detectors
Mikolajczyk, K., & Schmid, C. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000027790.02288.f2 · Cited by 3,436
ORB-SLAM: A Versatile and Accurate Monocular SLAM System
Mur-Artal, R., Montiel, J. M. M., & Tardos, J. D. (2015) · Zaguan (Universidad de Zaragoza) · Cited by 3,153
Unsupervised Monocular Depth Estimation with Left-Right Consistency
Godard, C., Aodha, O. M., & Brostow, G. J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.699 · Cited by 3,270
EPnP: An Accurate O(n) Solution to the PnP Problem
Lepetit, V., Moreno-Noguer, F., & Fua, P. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-008-0152-6 · Cited by 3,298
LOAM: Lidar Odometry and Mapping in Real-time
Zhang, J., & Singh, S. (2014) · Robotics: Science and Systems X · doi:10.15607/rss.2014.x.007 · Cited by 3,303
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
Eigen, D., Puhrsch, C., & Fergus, R. (2014) · arXiv · doi:10.48550/arxiv.1406.2283 · Cited by 3,442
Object retrieval with large vocabularies and fast spatial matching
Philbin, J. F., Chum, O., Isard, M., Šivic, J., & Zisserman, A. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383172 · Cited by 2,931
Self-citationA comparison of affine region detectors
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., & Gool, L. V. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-3848-x · Cited by 2,939
Self-citationSnavely, N., Seitz, S. M., & Szeliski, R. (2006) · ACM Transactions on Graphics · doi:10.1145/1141911.1141964 · Cited by 2,977
SuperGlue: Learning Feature Matching With Graph Neural Networks
Sarlin, P.-E., DeTone, D., Malisiewicz, T., & Rabinovich, A. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00499 · Cited by 3,234
Automatic Panoramic Image Stitching using Invariant Features
Brown, M., & Lowe, D. G. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-0002-3 · Cited by 2,709
Automatic generation and detection of highly reliable fiducial markers under occlusion
Garrido-Jurado, S., Muñoz‐Salinas, R., Madrid-Cuevas, F. J., & Marín-Jiménez, M. J. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.01.005 · Cited by 2,632
Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3008413 · Cited by 2,566
Modeling the World from Internet Photo Collections
Snavely, N., Seitz, S. M., & Szeliski, R. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-007-0107-3 · Cited by 2,212
KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera
Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R. A., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al. (2011) · ACM Symposium on User Interface Software and Technology · doi:10.1145/2047196.2047270 · Cited by 2,601
An efficient solution to the five-point relative pose problem
Nister, D. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.17 · Cited by 2,009
Optimization Algorithms on Matrix Manifolds
Absil, P.-A., Mahony, R. E., & Sepulchre, R. J. (2008) · Princeton University Press eBooks · doi:10.1515/9781400830244 · Cited by 3,715
UAV for 3D mapping applications: a review
Nex, F., & Fabio Remondino, A. (2013) · Applied Geomatics · doi:10.1007/s12518-013-0120-x · Cited by 1,894
Image change detection algorithms: a systematic survey
Radke, R. J., Andra, S., Al-Kofahi, O., & Roysam, B. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.838698 · Cited by 1,910
Li, S. Z., Jain, A. K., Deng, J., & service), S. (. (2011) · Springer eBooks · doi:10.1007/978-0-85729-932-1 · Cited by 1,867
AprilTag: A robust and flexible visual fiducial system
Olson, E. (2011) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2011.5979561 · Cited by 1,991
Knapitsch, A., Park, J., Zhou, Q.-Y., & Koltun, V. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073599 · Cited by 1,738
Scaramuzza, D., & Fraundorfer, F. (2011) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2011.943233 · Cited by 1,550
End-to-End Learning of Geometry and Context for Deep Stereo Regression
Kendall, A., Martirosyan, H., Dasgupta, S., Henry, P., Kennedy, R., Bachrach, A., & Bry, A. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.17 · Cited by 1,531
On-Manifold Preintegration for Real-Time Visual--Inertial Odometry
Forster, C., Carlone, L., Dellaert, F., & Scaramuzza, D. (2017) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2597321 · Cited by 1,502
The “Something Something” Video Database for Learning and Evaluating Visual Common Sense
Goyal, R., Kahou, S. E., Michalski, V., Materzynska, J., Westphal, S., Kim, H., Haenel, V., Fruend, I., Yianilos, P., Mueller-Freitag, M., et al. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.622 · Cited by 1,471
NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections
Martin-Brualla, R., Radwan, N., Sajjadi, M. S. M., Barron, J. T., Dosovitskiy, A., & Duckworth, D. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00713 · Cited by 1,498
Hedrick, T. L. (2008) · Bioinspiration & Biomimetics · doi:10.1088/1748-3182/3/3/034001 · Cited by 1,360
iSAM2: Incremental smoothing and mapping using the Bayes tree
Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J. J., & Dellaert, F. (2012) · The International Journal of Robotics Research · doi:10.1177/0278364911430419 · Cited by 1,362
D-NeRF: Neural Radiance Fields for Dynamic Scenes
Pumarola, A., Corona, E., Pons‐Moll, G., & Moreno-Noguer, F. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01018 · Cited by 1,402
Depth-image-based rendering (DIBR), compression, and transmission for a new approach on 3D-TV
Fehn, C. (2004) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.524762 · Cited by 1,355
Robust Wide Baseline Stereo from Maximally Stable Extremal Regions
Matas, J., Chum, O., Urban, M., & Pajdla, T. (2002) · Procedings of the British Machine Vision Conference 2002 · doi:10.5244/c.16.36 · Cited by 1,350
High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth
Scharstein, D., Hirschmüller, H., Kitajima, Y., Krathwohl, G., Nešić, N., Wang, X., & Westling, P. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11752-2_3 · Cited by 1,377
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
Wang, N., Zhang, Y., Li, Z., Fu, Y., Liu, W., & Jiang, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01252-6_4 · Cited by 1,362
GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose
Yin, Z., & Shi, J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00212 · Cited by 1,280
Mahler, J., Liang, J., Niyaz, S., Laskey, M., Doan, R., Liu, X., Aparicio, J., & Goldberg, K. Y. (2017) · Robotics: Science and Systems · doi:10.15607/rss.2017.xiii.058 · Cited by 1,434
Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration
Yang, J., Li, H., Campbell, D., & Jia, Y. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2513405 · Cited by 1,212
Advances in computational stereo
Brown, M., Burschka, D., & Hager, G. D. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1217603 · Cited by 1,136
Agarwal, S., Furukawa, Y., Snavely, N., Simon, I., Curless, B., Seitz, S. M., & Szeliski, R. (2011) · Communications of the ACM · doi:10.1145/2001269.2001293 · Cited by 1,077
Matching with PROSAC — Progressive Sample Consensus
Chum, O., & Matas, J. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2005.221 · Cited by 1,178
iSAM: Incremental Smoothing and Mapping
Kaess, M., Ranganathan, A., & Dellaert, F. (2008) · IEEE Transactions on Robotics · doi:10.1109/tro.2008.2006706 · Cited by 1,076
Segmentation and Recognition Using Structure from Motion Point Clouds
Brostow, G. J., Shotton, J., Fauqueur, J., & Cipolla, R. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_5 · Cited by 1,050
A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery
Amerini, I., Ballan, L., Caldelli, R., Bimbo, A. D., & Serra, G. (2011) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2011.2129512 · Cited by 1,068
Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review
Yeong, D. J., Velasco-Hernandez, G., Barry, J., & Walsh, J. L. (2021) · Sensors · doi:10.3390/s21062140 · Cited by 1,187
Robust Point Set Registration Using Gaussian Mixture Models
Jian, B., & Vemuri, B. C. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.223 · Cited by 682
High-speed 3D shape measurement with structured light methods: A review
Zhang, S. (2018) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2018.02.017 · Cited by 963
A performance evaluation of local descriptors
Mikolajczyk, K., & Schmid, C. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211478 · Cited by 955
A generic camera model and calibration method for conventional, wide-angle, and fish-eye lenses
Kannala, J., & Brandt, S. S. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.153 · Cited by 954
Heyden, A., & Pollefeys, M. (2005) · Malmö University Publications (Malmö University) · Cited by 1,124
Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing
Dellaert, F., & Kaess, M. (2006) · The International Journal of Robotics Research · doi:10.1177/0278364906072768 · Cited by 922
LightGlue: Local Feature Matching at Light Speed
Lindenberger, P., Sarlin, P.-E., & Pollefeys, M. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01616 · Cited by 1,165
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving
Bresson, G., Alsayed, Z., Yu, L., & Glaser, S. (2017) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2017.2749181 · Cited by 885
Ma, Y., Soatto, S., Košecká, J., & Sastry, S. (2004) · Interdisciplinary applied mathematics · doi:10.1007/978-0-387-21779-6 · Cited by 894
TEASER: Fast and Certifiable Point Cloud Registration
Yang, H., Shi, J., & Carlone, L. (2020) · IEEE Transactions on Robotics · doi:10.1109/tro.2020.3033695 · Cited by 958
Visual simultaneous localization and mapping: a survey
Fuentes-Pacheco, J., Ruiz-Ascencio, J., & Rendón-Mancha, J. M. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-012-9365-8 · Cited by 883
Visual Modeling with a Hand-Held Camera
Pollefeys, M., Gool, L. V., Vergauwen, M., Verbiest, F., Cornelis, K., Tops, J., & Koch, R. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000025798.50602.3a · Cited by 874
Differentiable Volumetric Rendering: Learning Implicit 3D Representations Without 3D Supervision
Niemeyer, M., Mescheder, L., Oechsle, M., & Geiger, A. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00356 · Cited by 854
Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval
Chum, O., Philbin, J. F., Šivic, J., Isard, M., & Zisserman, A. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408891 · Cited by 861
Self-citationOn benchmarking camera calibration and multi-view stereo for high resolution imagery
Strecha, C., von Hansen, W., Gool, L. V., Fua, P., & Thoennessen, U. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587706 · Cited by 824
Low-drift and real-time lidar odometry and mapping
Zhang, J., & Singh, S. (2017) · Auton. Robots · doi:10.1007/s10514-016-9548-2 · Cited by 778
Visual-Inertial Monocular SLAM With Map Reuse
Mur-Artal, R., & Tardós, J. D. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2653359 · Cited by 790
Segmentation-Based Image Copy-Move Forgery Detection Scheme
Li, J., Li, X., Yang, B., & Sun, X. (2014) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2014.2381872 · Cited by 810
Zhou, T., Tucker, R. P., Flynn, J., Fyffe, G., & Snavely, N. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201323 · Cited by 1,304
Geometric Loss Functions for Camera Pose Regression with Deep Learning
Kendall, A., & Cipolla, R. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.694 · Cited by 803
A review of computer vision–based structural health monitoring at local and global levels
Dong, C.-Z., & Catbas, F. (2020) · Structural Health Monitoring · doi:10.1177/1475921720935585 · Cited by 761
Inverse Depth Parametrization for Monocular SLAM
Civera, J., Davison, A. J., & Montiel, J. M. M. (2008) · IEEE Transactions on Robotics · doi:10.1109/tro.2008.2003276 · Cited by 768
Lourakis, M., & Argyros, A. (2009) · ACM Transactions on Mathematical Software · doi:10.1145/1486525.1486527 · Cited by 789
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
Feng, Y., Wu, F., Shao, X., Wang, Y., & Zhou, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_33 · Cited by 768
Structure from motion photogrammetry in physical geography
Smith, M. W., Carrivick, J. L., & Quincey, D. J. (2015) · Progress in Physical Geography Earth and Environment · doi:10.1177/0309133315615805 · Cited by 774
DeMoN: Depth and Motion Network for Learning Monocular Stereo
Ummenhofer, B., Zhou, H., Uhrig, J., Mayer, N., Ilg, E., Dosovitskiy, A., & Brox, T. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.596 · Cited by 712
Block-NeRF: Scalable Large Scene Neural View Synthesis
Tancik, M., Casser, V., Yan, X., Pradhan, S., Mildenhall, B., Srinivasan, P. P., Barron, J. T., & Kretzschmar, H. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00807 · Cited by 879
EpicFlow: Edge-preserving interpolation of correspondences for optical flow
Revaud, J., Weinzaepfel, P., Harchaoui, Z., & Schmid, C. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298720 · Cited by 757
Pose estimation for augmented reality: a hands-on survey
Marchand, E., Uchiyama, H., & Spindler, F. (2016) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2015.2513408 · Cited by 746
Barfoot, T. D. (2017) · Cambridge University Press eBooks · doi:10.1017/9781316671528 · Cited by 761
Gibson Env: Real-World Perception for Embodied Agents
Xia, F., Zamir, A. R., He, Z., Sax, A., Malik, J., & Savarese, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00945 · Cited by 763
Detailed Real-Time Urban 3D Reconstruction from Video
Pollefeys, M., Nistér, D., Frahm, J., Akbarzadeh, A., Mordohai, P., Clipp, B., Engels, C., Gallup, D., Kim, S.-J., Merrell, P. C., et al. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-007-0086-4 · Cited by 713
Criminisi, A. (2011) · now publishers, Inc. eBooks · doi:10.1561/9781601985415 · Cited by 723
A mobile vision system for robust multi-person tracking
Ess, A., Leibe, B., Schindler, K., & Van Gool, L. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587581 · Cited by 721
Intelligent multi-camera video surveillance: A review
Wang, X. (2012) · Pattern Recognition Letters · doi:10.1016/j.patrec.2012.07.005 · Cited by 694
Decomposing a scene into geometric and semantically consistent regions
Gould, S. J., Fulton, R., & Koller, D. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459211 · Cited by 697
Extrinsic calibration of a camera and laser range finder (improves camera calibration)
Zhang, Q., & Pless, R. B. (2005) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2004.1389752 · Cited by 697
Recovering Surface Layout from an Image
Hoiem, D., Efros, A. A., & Hebert, M. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-006-0031-y · Cited by 678
USAC: A Universal Framework for Random Sample Consensus
Raguram, R., Chum, O., Pollefeys, M., Matas, J., & Frahm, J. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.257 · Cited by 678
MUlti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking
Hong, Z., Chen, Z., Wang, C., Mei, X., Prokhorov, D. V., & Tao, D. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298675 · Cited by 654
Geometric context from a single image
Hoiem, D., Efros, A. A., & Hebert, M. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.107 · Cited by 666
Shape, Illumination, and Reflectance from Shading
Barron, J. T., & Malik, J. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2377712 · Cited by 660
Computer Vision for Autonomous Vehicles
Janai, J., Güney, F., Behl, A., & Geiger, A. (2020) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000079 · Cited by 707
Soft Rasterizer: A Differentiable Renderer for Image-Based 3D Reasoning
Liu, S., Chen, W., Li, T., et al. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00780 · Cited by 646
Metadata
- Authors
- Hartley, R., & Zisserman, A.
- Affiliations
- Australian National University (AU)
- University of Oxford (GB)
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2004-03-25
- OpenAlex
- W2033819227
- Cited by
- 21,070
- References
- 0
- Citations by year
- 2026: 534 · 2025: 702 · 2024: 706 · 2023: 719 · 2022: 657 · 2021: 991 · 2020: 989 · 2019: 1057 · 2018: 974 · 2017: 1026 · 2016: 1026 · 2015: 1120 · 2014: 1162 · 2013: 1172 · 2012: 1057
- FWCI
- 165.02
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Optical measurement and interference techniques
- Keywords
- multi-view geometry, projective geometry, fundamental matrix, trifocal tensor, scene reconstruction, photogrammetry, structure from motion, camera pose estimation, image correspondence
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review