Fetching the paper…
Reading the bibliography…
Synthetic datasets play a critical role in pre-training CNN models for optical flow, but they are painstaking to generate and hard to adapt to new applications.
Performance of optical flow techniques
J.L. Barron, D.J. Fleet, and S.S. Beauchemin · 1994
Earlier work this paper cites.
Representing moving images with layers
J. Y. A. Wang and E. H. Adelson · 1994
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
On the spatial statistics of optical flow
Stefan Roth and Michael J Black · 2007
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
Earlier work this paper cites.
Layered image motion with explicit occlusions, temporal consistency, and depth ordering
Deqing Sun, Erik B Sudderth, and Michael J Black · 2010
Earlier work this paper cites.
Computer vision: algorithms and applications
Richard Szeliski · 2010
Earlier work this paper cites.
A database and evaluation methodology for optical flow
S Baker, D. Scharstein, J. P. Lewis, S. Roth, M. J. Black, and R. Szeliski · 2011
Earlier work this paper cites.
A naturalistic open source movie for optical flow evaluation
D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black · 2012
Earlier work this paper cites.
Are we ready for autonomous driving? The KITTI vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
Earlier work this paper cites.
ImageNet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
Opendr: An approximate differentiable renderer
Matthew M Loper and Michael J Black · 2014
Earlier work this paper cites.
FlowNet: Learning optical flow with convolutional networks
Alexey Dosovitskiy, Philipp Fischery, Eddy Ilg, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, Thomas Brox, et al · 2015
Earlier work this paper cites.
Joint 3d estimation of vehicles and scene flow
Moritz Menze, Christian Heipke, and Andreas Geiger · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Virtual worlds as proxy for multi-object tracking analysis
Adrien Gaidon, Qiao Wang, Yohann Cabon, and Eleonora Vig · 2016
Earlier work this paper cites.
The cma evolution strategy: A tutorial
Nikolaus Hansen · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
The hci benchmark suite: Stereo and flow ground truth with uncertainties for urban autonomous driving
Daniel Kondermann, Rahul Nair, Katrin Honauer, Karsten Krispin, Jonas Andrulis, Alexander Brock, Burkhard Gussefeld, Mohsen Rahimimoghaddam, Sabine Hofmann, Claus Brenner, et al · 2016
Cited alongside, same era.
A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
Nikolaus Mayer, Eddy Ilg, Philip Häusser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox · 2016
Cited alongside, same era.
A benchmark dataset and evaluation methodology for video object segmentation
F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung · 2016
Cited alongside, same era.
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.
Iterative residual refinement for joint optical flow and occlusion estimation
Junhwa Hur and Stefan Roth · 2019
Later among the works it cites.
Sense: A shared encoder network for scene-flow estimation
Huaizu Jiang, Deqing Sun, Varun Jampani, Zhaoyang Lv, Erik Learned-Miller, and Jan Kautz · 2019
Later among the works it cites.
Meta-sim: Learning to generate synthetic datasets
Amlan Kar, Aayush Prakash, Ming-Yu Liu, Eric Cameracci, Justin Yuan, Matt Rusiniak, David Acuna, Antonio Torralba, and Sanja Fidler · 2019
Later among the works it cites.
Selflow: Self-supervised learning of optical flow
Pengpeng Liu, Michael Lyu, Irwin King, and Jia Xu · 2019
Later among the works it cites.
Models matter, so does training: An empirical study of cnns for optical flow estimation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, and Antonio M Lopez · 2016
Cited alongside, same era.
Carla: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
Cited alongside, same era.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Kilian Q Weinberger, and Laurens van der Maaten · 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.
Population based training of neural networks
Max Jaderberg, Valentin Dalibard, Simon Osindero, Wojciech M Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, Tim Green, Iain Dunning, Karen Simonyan, et al · 2017
Cited alongside, same era.
Optical flow estimation using a spatial pyramid network
Anurag Ranjan and Michael J Black · 2017
Cited alongside, same era.
Playing for benchmarks
Stephan R. Richter, Zeeshan Hayder, and Vladlen Koltun · 2017
Cited alongside, same era.
Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz · 2019
Later among the works it cites.
Volumetric correspondence networks for optical flow
Gengshan Yang and Deva Ramanan · 2019
Later among the works it cites.
Hierarchical discrete distribution decomposition for match density estimation
Zhichao Yin, Trevor Darrell, and Fisher Yu · 2019
Later among the works it cites.
Improving 3d object detection through progressive population based augmentation
Shuyang Cheng, Zhaoqi Leng, Ekin Dogus Cubuk, Barret Zoph, Chunyan Bai, Jiquan Ngiam, Yang Song, Benjamin Caine, Vijay Vasudevan, Congcong Li, et al · 2020
Later among the works it cites.
Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
Later among the works it cites.
What matters in unsupervised optical flow
Rico Jonschkowski, Austin Stone, Jonathan T Barron, Ariel Gordon, Kurt Konolige, and Anelia Angelova · 2020
Later among the works it cites.
The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale
Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, Tom Duerig, and Vittorio Ferrari · 2020
Later among the works it cites.
Learning multi-human optical flow
Anurag Ranjan, David T Hoffmann, Dimitrios Tzionas, Siyu Tang, Javier Romero, and Michael J Black · 2020
Later among the works it cites.
Accelerating 3d deep learning with pytorch3d
Nikhila Ravi, Jeremy Reizenstein, David Novotny, Taylor Gordon, Wan-Yen Lo, Justin Johnson, and Georgia Gkioxari · 2020
Later among the works it cites.
TF-RAFT: A tensorflow implementation of raft
Deqing Sun, Charles Herrmann, Varun Jampani, Michael Krainin, Forrester Cole, Austin Stone, Rico Jonschkowski, Ramin Zabih, William T. Freeman, and Ce Liu · 2020
Later among the works it cites.
RAFT: Recurrent all-pairs field transforms for optical flow
Zachary Teed and Jia Deng · 2020
Later among the works it cites.
Learning to generate 3d training data through hybrid gradient
Dawei Yang and Jia Deng · 2020
Later among the works it cites.
Domain-invariant stereo matching networks
Feihu Zhang, Xiaojuan Qi, Ruigang Yang, Victor Prisacariu, Benjamin Wah, and Philip Torr · 2020
Later among the works it cites.