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Recovering dense and long-range pixel motion in videos is a challenging problem.
Determining optical flow
Berthold KP Horn and Brian G Schunck · 1981
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An iterative image registration technique with an application to stereo vision
Bruce D Lucas and Takeo Kanade · 1981
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Detection and tracking of point
Carlo Tomasi and Takeo Kanade · 1991
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A framework for the robust estimation of optical flow
Michael J Black and Padmanabhan Anandan · 1993
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Good features to track
Jianbo Shi et al · 1994
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The computation of optical flow
Steven S. Beauchemin and John L. Barron · 1995
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Multi-frame optical flow estimation using subspace constraints
Michal Irani · 1999
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Three-dimensional scene flow
Sundar Vedula, Simon Baker, Peter Rander, Robert Collins, and Takeo Kanade · 1999
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On 3d scene flow and structure estimation
Ye Zhang and Chandra Kambhamettu · 2001
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Distinctive image features from scale-invariant keypoints
David G Lowe · 2004
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Particle video: Long-range motion estimation using point trajectories
Peter Sand and Seth J. Teller · 2006
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A variational method for scene flow estimation from stereo sequences
Frédéric Huguet and Frédéric Devernay · 2007
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Multi-view stereo reconstruction and scene flow estimation with a global image-based matching score
Jean-Philippe Pons, Renaud Keriven, and Olivier Faugeras · 2007
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A tutorial on spectral clustering
Ulrike Von Luxburg · 2007
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A duality based approach for realtime tv-l 1 optical flow
Christopher Zach, Thomas Pock, and Horst Bischof · 2007
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Large displacement optical flow
Thomas Brox, Christoph Bregler, and Jitendra Malik · 2009
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Layered image motion with explicit occlusions, temporal consistency, and depth ordering
Deqing Sun, Erik Sudderth, and Michael Black · 2010
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Kinecting the dots: Particle based scene flow from depth sensors
Simon Hadfield and Richard Bowden · 2011
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Kinectfusion: Real-time dense surface mapping and tracking
Richard A Newcombe, Shahram Izadi, Otmar Hilliges, David Molyneaux, David Kim, Andrew J Davison, Pushmeet Kohi, Jamie Shotton, Steve Hodges, and Andrew Fitzgibbon · 2011
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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3d scene flow estimation with a rigid motion prior
Christoph Vogel, Konrad Schindler, and Stefan Roth · 2011
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Modeling temporal coherence for optical flow
Sebastian Volz, Andres Bruhn, Levi Valgaerts, and Henning Zimmer · 2011
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Towards longer long-range motion trajectories
Michael Rubinstein, Ce Liu, and William T Freeman · 2012
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Rgb-d flow: Dense 3-d motion estimation using color and depth
Evan Herbst, Xiaofeng Ren, and Dieter Fox · 2013
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Real-time 3d reconstruction in dynamic scenes using point-based fusion
Maik Keller, Damien Lefloch, Martin Lambers, Shahram Izadi, Tim Weyrich, and Andreas Kolb · 2013
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Piecewise rigid scene flow
Christoph Vogel, Konrad Schindler, and Stefan Roth · 2013
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DeepFlow: Large displacement optical flow with deep matching
Philippe Weinzaepfel, Jerome Revaud, Zaid Harchaoui, and Cordelia Schmid · 2013
Cited alongside, same era.
Sphereflow: 6 dof scene flow from rgb-d pairs
Michael Hornacek, Andrew Fitzgibbon, and Carsten Rother · 2014
Cited alongside, same era.
Dense semi-rigid scene flow estimation from rgbd images
Julian Quiroga, Thomas Brox, Frédéric Devernay, and James Crowley · 2014
Cited alongside, same era.
Flownet: Learning optical flow with convolutional networks
Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazırbaş, Vladimir Golkov, Patrick Van der Smagt, Daniel Cremers, and Thomas Brox · 2015
Cited alongside, same era.
A primal-dual framework for real-time dense rgb-d scene flow
Mariano Jaimez, Mohamed Souiai, Javier Gonzalez-Jimenez, and Daniel Cremers · 2015
Cited alongside, same era.
Convolutional occupancy networks
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger · 2020
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Raft: Recurrent all-pairs field transforms for optical flow
Zachary Teed and Jia Deng · 2020
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Flownet3d++: Geometric losses for deep scene flow estimation
Zirui Wang, Shuda Li, Henry Howard-Jenkins, Victor Prisacariu, and Min Chen · 2020
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Pointpwc-net: Cost volume on point clouds for (self-) supervised scene flow estimation
Wenxuan Wu, Zhi Yuan Wang, Zhuwen Li, Wei Liu, and Li Fuxin · 2020
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Neural deformation graphs for globally-consistent non-rigid reconstruction
Aljaž Božič, Pablo Palafox, Michael Zollhöfer, Justus Thies, Angela Dai, and Matthias Nießner · 2021
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Perceiver io: A general architecture for structured inputs & outputs
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Optical flow with geometric occlusion estimation and fusion of multiple frames
Ryan Kennedy and Camillo J Taylor · 2015
Cited alongside, same era.
Object scene flow for autonomous vehicles
Moritz Menze and Andreas Geiger · 2015
Cited alongside, same era.
Dynamicfusion: Reconstruction and tracking of non-rigid scenes in real-time
Richard A. Newcombe, Dieter Fox, and Steven M. Seitz · 2015
Cited alongside, same era.
N. Mayer, E. Ilg, P. Häusser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox · 2016
Cited alongside, same era.
Optical flow with semantic segmentation and localized layers
Laura Sevilla-Lara, Deqing Sun, Varun Jampani, and Michael J Black · 2016
Cited alongside, same era.
Quo vadis, action recognition? a new model and the kinetics dataset
Joao Carreira and Andrew Zisserman · 2017
Cited alongside, same era.
Flownet 2.0: Evolution of optical flow estimation with deep networks
Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox · 2017
Cited alongside, same era.
Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, et al · 2021
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Layered neural atlases for consistent video editing
Yoni Kasten, Dolev Ofri, Oliver Wang, and Tali Dekel · 2021
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Beyond pick-and-place: Tackling robotic stacking of diverse shapes
Alex X Lee, Coline Manon Devin, Yuxiang Zhou, Thomas Lampe, Konstantinos Bousmalis, Jost Tobias Springenberg, Arunkumar Byravan, Abbas Abdolmaleki, Nimrod Gileadi, David Khosid, et al · 2021
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Vision transformers for dense prediction
René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun · 2021
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Raft-3d: Scene flow using rigid-motion embeddings
Zachary Teed and Jia Deng · 2021
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Efficient geometry-aware 3d generative adversarial networks
Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al · 2022
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Tap-vid: A benchmark for tracking any point in a video
Carl Doersch, Ankush Gupta, Larisa Markeeva, Adrià Recasens, Lucas Smaira, Yusuf Aytar, João Carreira, Andrew Zisserman, and Yi Yang · 2022
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Particle video revisited: Tracking through occlusions using point trajectories
Adam W. Harley, Zhaoyuan Fang, and Katerina Fragkiadaki · 2022
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Flowformer: A transformer architecture for optical flow
Zhaoyang Huang, Xiaoyu Shi, Chao Zhang, Qiang Wang, Ka Chun Cheung, Hongwei Qin, Jifeng Dai, and Hongsheng Li · 2022
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Cadex: Learning canonical deformation coordinate space for dynamic surface representation via neural homeomorphism
Jiahui Lei and Kostas Daniilidis · 2022
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Occlusionfusion: Occlusion-aware motion estimation for real-time dynamic 3d reconstruction
Wenbin Lin, Chengwei Zheng, Jun-Hai Yong, and Feng Xu · 2022
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Deformable sprites for unsupervised video decomposition
Vickie Ye, Zhengqi Li, Richard Tucker, Angjoo Kanazawa, and Noah Snavely · 2022
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Global matching with overlapping attention for optical flow estimation
Shiyu Zhao, Long Zhao, Zhixing Zhang, Enyu Zhou, and Dimitris Metaxas · 2022
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Zoedepth: Zero-shot transfer by combining relative and metric depth
Shariq Farooq Bhat, Reiner Birkl, Diana Wofk, Peter Wonka, and Matthias Müller · 2023
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Context-tap: Tracking any point demands spatial context features
Weikang Bian, Zhaoyang Huang, Xiaoyu Shi, Yitong Dong, Yijin Li, and Hongsheng Li · 2023
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Midas v3. 1–a model zoo for robust monocular relative depth estimation
Reiner Birkl, Diana Wofk, and Matthias Müller · 2023
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TAPIR: tracking any point with per-frame initialization and temporal refinement
Carl Doersch, Yi Yang, Mel Vecerík, Dilara Gokay, Ankush Gupta, Yusuf Aytar, João Carreira, and Andrew Zisserman · 2023
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Cotracker: It is better to track together
Nikita Karaev, Ignacio Rocco, Benjamin Graham, Natalia Neverova, Andrea Vedaldi, and Christian Rupprecht · 2023
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Mft: Long-term tracking of every pixel
Michal Neoral, Jonáš Šerỳch, and Jiří Matas · 2023
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Flowformer++: Masked cost volume autoencoding for pretraining optical flow estimation
Xiaoyu Shi, Zhaoyang Huang, Dasong Li, Manyuan Zhang, Ka Chun Cheung, Simon See, Hongwei Qin, Jifeng Dai, and Hongsheng Li · 2023
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Pointodyssey: A large-scale synthetic dataset for long-term point tracking
Yang Zheng, Adam W Harley, Bokui Shen, Gordon Wetzstein, and Leonidas J Guibas · 2023
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