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A Large Dataset to Train Convolutional Networks for Disparity, Optical
  Flow, and Scene Flow Estimation

A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation

7 December 2015
N. Mayer
Eddy Ilg
Philip Häusser
Philipp Fischer
Daniel Cremers
Alexey Dosovitskiy
Thomas Brox
    3DPC
ArXiv (abs)PDFHTML

Papers citing "A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation"

21 / 1,171 papers shown
Title
Hybrid Learning of Optical Flow and Next Frame Prediction to Boost
  Optical Flow in the Wild
Hybrid Learning of Optical Flow and Next Frame Prediction to Boost Optical Flow in the Wild
Nima Sedaghat
Mohammadreza Zolfaghari
Thomas Brox
77
16
0
12 Dec 2016
Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object
  Parsing
Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing
Chi Li
M. Zia
Quoc-Huy Tran
Xiang Yu
Gregory Hager
Manmohan Chandraker
3DPC3DV
107
91
0
08 Dec 2016
Scene Flow Estimation: A Survey
Scene Flow Estimation: A Survey
Zike Yan
Xuezhi Xiang
65
29
0
08 Dec 2016
DeMoN: Depth and Motion Network for Learning Monocular Stereo
DeMoN: Depth and Motion Network for Learning Monocular Stereo
Benjamin Ummenhofer
Huizhong Zhou
J. Uhrig
N. Mayer
Eddy Ilg
Alexey Dosovitskiy
Thomas Brox
3DVMDE
141
703
0
07 Dec 2016
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
Eddy Ilg
N. Mayer
Tonmoy Saikia
Margret Keuper
Alexey Dosovitskiy
Thomas Brox
3DPC
268
3,083
0
06 Dec 2016
Deep Stereo Matching with Dense CRF Priors
Deep Stereo Matching with Dense CRF Priors
Ron Slossberg
Aaron Wetzler
Ron Kimmel
3DV
20
5
0
06 Dec 2016
Semi-supervised learning of deep metrics for stereo reconstruction
Semi-supervised learning of deep metrics for stereo reconstruction
S. Tulyakov
A. Ivanov
François Fleuret
3DVSSL
16
1
0
03 Dec 2016
Procedural Generation of Videos to Train Deep Action Recognition
  Networks
Procedural Generation of Videos to Train Deep Action Recognition Networks
César Roberto de Souza
Adrien Gaidon
Yohann Cabon
A. Peña
84
144
0
02 Dec 2016
TorontoCity: Seeing the World with a Million Eyes
TorontoCity: Seeing the World with a Million Eyes
Shenlong Wang
Min Bai
Gellért Máttyus
Hang Chu
Wenjie Luo
Binh Yang
Justin Liang
Joel Cheverie
Sanja Fidler
R. Urtasun
3DVViT
77
178
0
01 Dec 2016
End-to-End Training of Hybrid CNN-CRF Models for Stereo
End-to-End Training of Hybrid CNN-CRF Models for Stereo
Patrick Knöbelreiter
Christian Reinbacher
Alexander Shekhovtsov
Thomas Pock
3DV
113
132
0
30 Nov 2016
Surveillance Video Parsing with Single Frame Supervision
Surveillance Video Parsing with Single Frame Supervision
Si Liu
Changhu Wang
Ruihe Qian
Han Yu
Renda Bao
80
59
0
29 Nov 2016
End-to-end Learning of Cost-Volume Aggregation for Real-time Dense
  Stereo
End-to-end Learning of Cost-Volume Aggregation for Real-time Dense Stereo
Andrey Kuzmin
Dmitry Mikushin
Victor Lempitsky
3DV
43
25
0
17 Nov 2016
Optical Flow Estimation using a Spatial Pyramid Network
Optical Flow Estimation using a Spatial Pyramid Network
Anurag Ranjan
Michael J. Black
3DPC
125
1,221
0
03 Nov 2016
Real-time Halfway Domain Reconstruction of Motion and Geometry
Real-time Halfway Domain Reconstruction of Motion and Geometry
Lucas Thies
Michael Zollhöfer
Christian Richardt
Christian Theobalt
G. Greiner
3DH
54
2
0
23 Oct 2016
Quick and energy-efficient Bayesian computing of binocular disparity
  using stochastic digital signals
Quick and energy-efficient Bayesian computing of binocular disparity using stochastic digital signals
Alexandre Coninx
P. Bessière
J. Droulez
3DV
16
4
0
14 Sep 2016
Unsupervised Monocular Depth Estimation with Left-Right Consistency
Unsupervised Monocular Depth Estimation with Left-Right Consistency
Clément Godard
Oisin Mac Aodha
Gabriel J. Brostow
MDE
159
2,890
0
13 Sep 2016
Back to Basics: Unsupervised Learning of Optical Flow via Brightness
  Constancy and Motion Smoothness
Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness
Jason J. Yu
Adam W. Harley
Konstantinos G. Derpanis
83
410
0
20 Aug 2016
Playing for Data: Ground Truth from Computer Games
Playing for Data: Ground Truth from Computer Games
Stephan R. Richter
Vibhav Vineet
Stefan Roth
V. Koltun
VLM
126
2,018
0
07 Aug 2016
CNN-based Patch Matching for Optical Flow with Thresholded Hinge
  Embedding Loss
CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss
C. Bailer
Kiran Varanasi
Didier Stricker
115
63
0
27 Jul 2016
Exploiting Semantic Information and Deep Matching for Optical Flow
Exploiting Semantic Information and Deep Matching for Optical Flow
Min Bai
Wenjie Luo
Kaustav Kundu
R. Urtasun
3DPC
43
5
0
06 Apr 2016
Enabling Depth-driven Visual Attention on the iCub Humanoid Robot:
  Instructions for Use and New Perspectives
Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives
Giulia Pasquale
Tanis Mar
C. Ciliberto
Lorenzo Rosasco
Lorenzo Natale
54
22
0
23 Sep 2015
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