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A reliable stereo algorithm is critical for many robotics applications.
Performance of optical flow techniques
J. L. Barron, D. J. Fleet, and S. S. Beauchemin · 1994
Earlier work this paper cites.
An experimental comparison of stereo algorithms
R. Szeliski and R. Zabih · 1999
Earlier work this paper cites.
A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
D. Scharstein and R. Szeliski · 2002
Earlier work this paper cites.
Dense stereomatching algorithm performance for view prediction and structure reconstruction
J. Kostková, J. Čech, and R. Šára · 2003
Earlier work this paper cites.
High-accuracy stereo depth maps using structured light
D. Scharstein and R. Szeliski · 2003
Earlier work this paper cites.
Accurate and efficient stereo processing by semi-global matching and mutual information
H. Hirschmuller · 2005
Earlier work this paper cites.
Evaluation of cost functions for stereo matching
H. Hirschmuller and D. Scharstein · 2007
Earlier work this paper cites.
Evaluation of constructable match cost measures for stereo correspondence using cluster ranking
D. Neilson and Y.-H. Yang · 2008
Earlier work this paper cites.
Near real-time stereo for weakly-textured scenes
Q. Yang, C. Engels, and A. Akbarzadeh · 2008
Earlier work this paper cites.
Robustness evaluation of stereo algorithms on long stereo sequences
S. Morales, T. Vaudrey, and R. Klette · 2009
Earlier work this paper cites.
Efficient large-scale stereo matching
A. Geiger, M. Roser, and R. Urtasun · 2010
Earlier work this paper cites.
A database and evaluation methodology for optical flow
S. Baker, D. Scharstein, J. 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
A. Geiger, P. Lenz, and R. Urtasun · 2012
Earlier work this paper cites.
Outdoor stereo camera system for the generation of real-world benchmark data sets
S. Meister, B. Jähne, and D. Kondermann · 2012
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Towards a simulation driven stereo vision system
M. Peris, S. Martull, A. Maki, Y. Ohkawa, and K. Fukui · 2012
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Synthesizing real world stereo challenges
R. Haeusler and D. Kondermann · 2013
Cited alongside, same era.
Real shading in unreal engine 4
B. Karis and E. Games · 2013
Cited alongside, same era.
Constant time weighted median filtering for stereo matching and beyond
Z. Ma, K. He, Y. Wei, J. Sun, and E. Wu · 2013
Cited alongside, same era.
High-resolution stereo datasets with subpixel-accurate ground truth
D. Scharstein, H. Hirschmüller, Y. Kitajima, G. Krathwohl, N. Nešić, X. Wang, and P. Westling · 2014
Cited alongside, same era.
CV-HAZOP: Introducing test data validation for computer vision
O. Zendel, M. Murschitz, M. Humenberger, and W. Herzner · 2015
Later among the works it cites.
iLab-20M: A large-scale controlled object dataset to investigate deep learning
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Virtual worlds as proxy for multi-object tracking analysis
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A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
N. Mayer, E. Ilg, P. Häusser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox · 2016
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UnrealCV: Connecting computer vision to unreal engine
W. Qiu and A. Yuille · 2016
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Low-level vision by consensus in a spatial hierarchy of regions
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Displets: Resolving stereo ambiguities using object knowledge
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The HCI stereo metrics: Geometry-aware performance analysis of stereo algorithms
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Object scene flow for autonomous vehicles
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Reflection modeling for passive stereo
R. Nair, A. Fitzgibbon, D. Kondermann, and C. Rother · 2015
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Playing for data: Ground truth from computer games
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The SYNTHIA dataset: A large collection of synthetic images for semantic segmentation of urban scenes
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Adversarial examples for semantic segmentation and object detection
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Training deep networks with synthetic data: Bridging the reality gap by domain randomization
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