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Stereo Matching

Stereo Matching is one of the core technologies in computer vision, which recovers 3D structures of real world from 2D images. It has been widely used in areas such as autonomous driving, augmented reality and robotics navigation. Given a pair of rectified stereo images, the goal of Stereo Matching is to compute the disparity for each pixel in the reference image, where disparity is defined as the horizontal displacement between a pair of corresponding pixels in the left and right images.

Source: Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching

Papers

Showing 91100 of 517 papers

TitleStatusHype
Stereo Matching Based on Visual Sensitive InformationCode1
CFNet: Cascade and Fused Cost Volume for Robust Stereo MatchingCode1
SMD-Nets: Stereo Mixture Density NetworksCode1
YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D DetectionCode1
ES-Net: An Efficient Stereo Matching NetworkCode1
3D Surface Reconstruction From Multi-Date Satellite ImagesCode1
On the confidence of stereo matching in a deep-learning era: a quantitative evaluationCode1
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow EstimationCode1
Hierarchical Neural Architecture Search for Deep Stereo MatchingCode1
Matching-space Stereo Networks for Cross-domain GeneralizationCode1
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