Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes
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Hongdong Li | Yiran Zhong | Yuchao Dai | Pan Ji | Jianyuan Wang | Yuchao Dai | Hongdong Li | Pan Ji | Yiran Zhong | Jianyuan Wang
[1] Weiyu Xu,et al. Necessary and sufficient conditions for success of the nuclear norm heuristic for rank minimization , 2008, 2008 47th IEEE Conference on Decision and Control.
[2] Michael J. Black,et al. Optical Flow in Mostly Rigid Scenes , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] J.-Y. Bouguet,et al. Pyramidal implementation of the lucas kanade feature tracker , 1999 .
[4] Rachid Deriche,et al. Computing Optical Flow via Variational Techniques , 1999, SIAM J. Appl. Math..
[5] Vladimir Kolmogorov,et al. Computing visual correspondence with occlusions using graph cuts , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[6] Yi Yang,et al. Occlusion Aware Unsupervised Learning of Optical Flow , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[7] Konstantinos G. Derpanis,et al. Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness , 2016, ECCV Workshops.
[8] L. Rudin,et al. Nonlinear total variation based noise removal algorithms , 1992 .
[9] Daniel Cremers,et al. Structure- and motion-adaptive regularization for high accuracy optic flow , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[10] Yong Yu,et al. Robust Recovery of Subspace Structures by Low-Rank Representation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[11] Shuicheng Yan,et al. Robust and Efficient Subspace Segmentation via Least Squares Regression , 2012, ECCV.
[12] Richard Szeliski,et al. A Database and Evaluation Methodology for Optical Flow , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[13] Thomas Brox,et al. FlowNet: Learning Optical Flow with Convolutional Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[14] Joachim Weickert,et al. A Variational Model for the Joint Recovery of the Fundamental Matrix and the Optical Flow , 2008, DAGM-Symposium.
[15] Michael J. Black,et al. Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Christian Heipke,et al. Discrete Optimization for Optical Flow , 2015, GCPR.
[17] Jiaolong Yang,et al. Dense, accurate optical flow estimation with piecewise parametric model , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Rachid Deriche,et al. Symmetrical Dense Optical Flow Estimation with Occlusions Detection , 2002, International Journal of Computer Vision.
[19] Jan Kautz,et al. PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[20] Michael J. Black,et al. Secrets of optical flow estimation and their principles , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[21] Zhichao Yin,et al. GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[22] Robert C. Bolles,et al. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.
[23] Michael J. Black,et al. Supplementary Material for Unsupervised Learning of Multi-Frame Optical Flow with Occlusions , 2018 .
[24] Andreas Geiger,et al. Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[25] Bernhard P. Wrobel,et al. Multiple View Geometry in Computer Vision , 2001 .
[26] Michael J. Black,et al. Optical Flow Estimation Using a Spatial Pyramid Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27] René Vidal,et al. Clustering disjoint subspaces via sparse representation , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.
[28] Cordelia Schmid,et al. EpicFlow: Edge-preserving interpolation of correspondences for optical flow , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Christopher Hunt,et al. Notes on the OpenSURF Library , 2009 .
[30] Raquel Urtasun,et al. Robust Monocular Epipolar Flow Estimation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[31] Michael J. Black,et al. Adversarial Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation , 2018, ArXiv.
[32] Zhuwen Li,et al. Perspective Motion Segmentation via Collaborative Clustering , 2013, 2013 IEEE International Conference on Computer Vision.
[33] Horst Bischof,et al. A Duality Based Approach for Realtime TV-L1 Optical Flow , 2007, DAGM-Symposium.
[34] Bingbing Ni,et al. Unsupervised Deep Learning for Optical Flow Estimation , 2017, AAAI.
[35] Hongdong Li,et al. Efficient dense subspace clustering , 2014, IEEE Winter Conference on Applications of Computer Vision.
[36] Hongdong Li,et al. Robust Multi-Body Feature Tracker: A Segmentation-Free Approach , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Daniel Rueckert,et al. Dense Multi-frame Optic Flow for Non-rigid Objects Using Subspace Constraints , 2010, ACCV.
[38] Berthold K. P. Horn,et al. Determining Optical Flow , 1981, Other Conferences.
[39] René Vidal,et al. Sparse Subspace Clustering: Algorithm, Theory, and Applications , 2012, IEEE transactions on pattern analysis and machine intelligence.
[40] Jia-Bin Huang,et al. DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency , 2018, ECCV.
[41] Thomas Brox,et al. High Accuracy Optical Flow Estimation Based on a Theory for Warping , 2004, ECCV.
[42] Michael J. Black,et al. A Naturalistic Open Source Movie for Optical Flow Evaluation , 2012, ECCV.
[43] Andreas Geiger,et al. Object scene flow for autonomous vehicles , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[44] Andrew Zisserman,et al. Spatial Transformer Networks , 2015, NIPS.
[45] Daniel Cremers,et al. An Improved Algorithm for TV-L 1 Optical Flow , 2009, Statistical and Geometrical Approaches to Visual Motion Analysis.
[46] Thomas Brox,et al. FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[47] Stefan Roth,et al. UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss , 2017, AAAI.