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We propose a novel deep learning architecture for regressing disparity from a rectified pair of stereo images.
Computational stereo
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H. Hirschmüller · 2008
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Stereo vision and laser odometry for autonomous helicopters in gps-denied indoor environments
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Efficient large-scale stereo matching
A. Geiger, M. Roser, and R. Urtasun · 2010
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Efficient Large-Scale Stereo Matching
A. Geiger, M. Roser, and R. Urtasun · 2010
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PatchMatch Stereo-Stereo Matching with Slanted Support Windows
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Are we ready for autonomous driving? the kitti vision benchmark suite
A. Geiger, P. Lenz, and R. Urtasun · 2012
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T. Tieleman and G. Hinton · 2012
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Ensemble Learning for Confidence Measures in Stereo Vision
R. Haeusler, R. Nair, and D. Kondermann · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
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Neural machine translation by jointly learning to align and translate
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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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 · 2015
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Object scene flow for autonomous vehicles
M. Menze and A. Geiger · 2015
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Leveraging stereo matching with learning-based confidence measures
M. G. Park and K. J. Yoon · 2015
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Learning to compare image patches via convolutional neural networks
S. Zagoruyko and N. Komodakis · 2015
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Computing the stereo matching cost with a convolutional neural network
J. Žbontar and Y. Le Cun · 2015
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D. Bahdanau, K. Cho, and Y. Bengio · 2014
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Depth map prediction from a single image using a multi-scale deep network
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Rich feature hierarchies for accurate object detection and semantic segmentation
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Efficient joint segmentation, occlusion labeling, stereo and flow estimation
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Visualizing and understanding convolutional networks
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
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Computing the stereo matching cost with a convolutional neural network
J. Zbontar and Y. LeCun · 2015
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Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
J. Žbontar and Y. LeCun · 2015
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A deep visual correspondence embedding model for stereo matching costs
Z. Chen, X. Sun, L. Wang, Y. Yu, and C. Huang · 2016
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DeepStereo: Learning to Predict New Views from the World’s Imagery
J. Flynn, I. Neulander, J. Philbin, and N. Snavely · 2016
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Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Efficient deep learning for stereo matching
W. Luo, A. G. Schwing, and R. Urtasun · 2016
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Efficient Deep Learning for Stereo Matching
W. Luo, A. G. Schwing, and R. Urtasun · 2016
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N.Mayer, E.Ilg, P.Häusser, P.Fischer, D.Cremers, A.Dosovitskiy, and T.Brox · 2016
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Patch based confidence prediction for dense disparity map
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Stereo matching by training a convolutional neural network to compare image patches
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