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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 251260 of 517 papers

TitleStatusHype
Self-Supervised Intensity-Event Stereo Matching0
Self-Supervised Learning for Stereo Matching with Self-Improving Ability0
Semantic See-Through Rendering on Light Fields0
Semantic Stereo Matching With Pyramid Cost Volumes0
Semi-dense Stereo Matching using Dual CNNs0
Semi-Global Stereo Matching with Surface Orientation Priors0
Semi-synthesis: A fast way to produce effective datasets for stereo matching0
SemStereo: Semantic-Constrained Stereo Matching Network for Remote Sensing0
Appearance and Shape from Water Reflection0
Shift Convolution Network for Stereo Matching0
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