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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 111120 of 517 papers

TitleStatusHype
ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo SystemsCode1
Self-Assessed Generation: Trustworthy Label Generation for Optical Flow and Stereo Matching in Real-worldCode1
AANet: Adaptive Aggregation Network for Efficient Stereo MatchingCode1
SOS: Stereo Matching in O(1) with Slanted Support WindowsCode1
Digging Into Uncertainty-based Pseudo-label for Robust Stereo MatchingCode1
ELFNet: Evidential Local-global Fusion for Stereo MatchingCode1
StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo MatchingCode1
Stereo Hybrid Event-Frame (SHEF) Cameras for 3D PerceptionCode1
FADNet: A Fast and Accurate Network for Disparity EstimationCode1
Hierarchical Neural Architecture Search for Deep Stereo MatchingCode1
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