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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

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Showing 101–110 of 517 papers

TitleStatusHype
Do End-to-end Stereo Algorithms Under-utilize Information?Code1
Adaptive confidence thresholding for monocular depth estimationCode1
Parallax Attention for Unsupervised Stereo Correspondence LearningCode1
Stereo Plane SLAM Based on Intersecting LinesCode1
Learning Stereo Matchability in Disparity Regression NetworksCode1
Learning Stereo from Single ImagesCode1
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo MatchingCode1
A Multi-spectral Dataset for Evaluating Motion Estimation SystemsCode1
MTStereo 2.0: improved accuracy of stereo depth estimation withMax-treesCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
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