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We present a self-supervised learning framework to estimate the individual object motion and monocular depth from video.
Three-dimensional scene flow
Sundar Vedula, Simon Baker, Peter Rander, Robert Collins, and Takeo Kanade · 1999
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Learning depth from single monocular images
Ashutosh Saxena, Sung H Chung, and Andrew Y Ng · 2006
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Single image depth estimation from predicted semantic labels
Beyang Liu, Stephen Gould, and Daphne Koller · 2010
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Stereoscan: Dense 3d reconstruction in real-time
Andreas Geiger, Julius Ziegler, and Christoph Stiller · 2011
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Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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Piecewise rigid scene flow
Christoph Vogel, Konrad Schindler, and Stefan Roth · 2013
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Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
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Pulling things out of perspective
Lubor Ladicky, Jianbo Shi, and Marc Pollefeys · 2014
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Efficient joint segmentation, occlusion labeling, stereo and flow estimation
Koichiro Yamaguchi, David McAllester, and Raquel Urtasun · 2014
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Flownet: Learning optical flow with convolutional networks
Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick Van Der Smagt, Daniel Cremers, and Thomas Brox · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Depth and surface normal estimation from monocular images using regression on deep features and hierarchical crfs
Bo Li, Chunhua Shen, Yuchao Dai, Anton Van Den Hengel, and Mingyi He · 2015
Cited alongside, same era.
Object scene flow for autonomous vehicles
Moritz Menze and Andreas Geiger · 2015
Cited alongside, same era.
Tensorflow: a system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
Cited alongside, same era.
The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
Cited alongside, same era.
Deeper depth prediction with fully convolutional residual networks
Iro Laina, Christian Rupprecht, Vasileios Belagiannis, Federico Tombari, and Nassir Navab · 2016
Cited alongside, same era.
Fusion of stereo and still monocular depth estimates in a self-supervised learning context
Diogo Martins, Kevin Van Hecke, and Guido De Croon · 2018
Later among the works it cites.
Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume
Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz · 2018
Later among the works it cites.
Geonet: Unsupervised learning of dense depth, optical flow and camera pose
Zhichao Yin and Jianping Shi · 2018
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Lidar-flow: Dense scene flow estimation from sparse lidar and stereo images
Ramy Battrawy, René Schuster, Oliver Wasenmüller, Qing Rao, and Didier Stricker · 2019
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Learning independent object motion from unlabelled stereoscopic videos
Zhe Cao, Abhishek Kar, Christian Häne, and Jitendra Malik · 2019
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Depth prediction without the sensors: Leveraging structure for unsupervised learning from monocular videos
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Unsupervised monocular depth estimation with left-right consistency
Clément Godard, Oisin Mac Aodha, and Gabriel J Brostow · 2017
Cited alongside, same era.
Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 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.
Unsupervised learning of depth and ego-motion from video
Tinghui Zhou, Matthew Brown, Noah Snavely, and David G Lowe · 2017
Cited alongside, same era.
Digging into self-supervised monocular depth estimation
Clément Godard, Oisin Mac Aodha, Michael Firman, and Gabriel Brostow · 2018
Cited alongside, same era.
Every pixel counts++: Joint learning of geometry and motion with 3d holistic understanding
Chenxu Luo, Zhenheng Yang, Peng Wang, Yang Wang, Wei Xu, Ram Nevatia, and Alan Yuille · 2018
Cited alongside, same era.
Unsupervised learning of depth and ego-motion from monocular video using 3d geometric constraints
Reza Mahjourian, Martin Wicke, and Anelia Angelova · 2018
Cited alongside, same era.
Vincent Casser, Soeren Pirk, Reza Mahjourian, and Anelia Angelova · 2019
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Learning residual flow as dynamic motion from stereo videos
Seokju Lee, Sunghoon Im, Stephen Lin, and In So Kweon · 2019
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Deep rigid instance scene flow
Wei-Chiu Ma, Shenlong Wang, Rui Hu, Yuwen Xiong, and Raquel Urtasun · 2019
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Competitive collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation
Anurag Ranjan, Varun Jampani, Lukas Balles, Kihwan Kim, Deqing Sun, Jonas Wulff, and Michael J Black · 2019
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Dispsegnet: Leveraging semantics for end-to-end learning of disparity estimation from stereo imagery
Junming Zhang, Katherine A Skinner, Ram Vasudevan, and Matthew Johnson-Roberson · 2019
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Don’t forget the past: Recurrent depth estimation from monocular video
Vaishakh Patil, Wouter Van Gansbeke, Dengxin Dai, and Luc Van Gool · 2020
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