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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 301–310 of 517 papers

TitleStatusHype
An Inference Algorithm for Multi-Label MRF-MAP Problems with Clique Size 100Code0
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo MatchingCode1
Real-time Dense Reconstruction of Tissue Surface from Stereo Optical Video—0
Real-time Surface Deformation Recovery from Stereo Videos—0
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
PCW-Net: Pyramid Combination and Warping Cost Volume for Stereo MatchingCode1
FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications—0
Content-Aware Inter-Scale Cost Aggregation for Stereo Matching—0
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