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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 5160 of 517 papers

TitleStatusHype
Global Occlusion-Aware Transformer for Robust Stereo MatchingCode1
Revisiting Depth Completion from a Stereo Matching Perspective for Cross-domain GeneralizationCode1
MC-Stereo: Multi-peak Lookup and Cascade Search Range for Stereo MatchingCode1
When Epipolar Constraint Meets Non-local Operators in Multi-View StereoCode1
SparseSat-NeRF: Dense Depth Supervised Neural Radiance Fields for Sparse Satellite ImagesCode1
ELFNet: Evidential Local-global Fusion for Stereo MatchingCode1
Digging Into Uncertainty-based Pseudo-label for Robust Stereo MatchingCode1
Adaptive Multi-Modal Cross-Entropy Loss for Stereo MatchingCode1
MVPSNet: Fast Generalizable Multi-view Photometric StereoCode1
Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty EstimationCode1
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