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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 3140 of 517 papers

TitleStatusHype
BANet: Bilateral Aggregation Network for Mobile Stereo MatchingCode2
GenStereo: Towards Open-World Generation of Stereo Images and Unsupervised MatchingCode2
Learning Robust Stereo Matching in the Wild with Selective Mixture-of-ExpertsCode2
Event-based Stereo Depth Estimation: A SurveyCode2
ChiTransformer:Towards Reliable Stereo from CuesCode1
Adaptive confidence thresholding for monocular depth estimationCode1
Do End-to-end Stereo Algorithms Under-utilize Information?Code1
ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo SystemsCode1
AANet: Adaptive Aggregation Network for Efficient Stereo MatchingCode1
ELFNet: Evidential Local-global Fusion for Stereo MatchingCode1
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