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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 1120 of 517 papers

TitleStatusHype
Learning Robust Stereo Matching in the Wild with Selective Mixture-of-ExpertsCode2
ESMStereo: Enhanced ShuffleMixer Disparity Upsampling for Real-Time and Accurate Stereo MatchingCode2
GenStereo: Towards Open-World Generation of Stereo Images and Unsupervised MatchingCode2
BANet: Bilateral Aggregation Network for Mobile Stereo MatchingCode2
Motif Channel Opened in a White-Box: Stereo Matching via Motif Correlation GraphCode2
Event-based Stereo Depth Estimation: A SurveyCode2
Temporally Consistent Stereo MatchingCode2
MoCha-Stereo: Motif Channel Attention Network for Stereo MatchingCode2
Robust Confidence Intervals in Stereo Matching using Possibility TheoryCode2
Neural Markov Random Field for Stereo MatchingCode2
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