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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 471480 of 517 papers

TitleStatusHype
Rotational Crossed-Slit Light Field0
The Global Patch Collider0
Efficient Deep Learning for Stereo MatchingCode0
Stereo Matching With Color and Monochrome Cameras in Low-Light Conditions0
Depth From Semi-Calibrated Stereo and Defocus0
Detecting Ground Control Points via Convolutional Neural Network for Stereo Matching0
Epipolar Geometry Based On Line Similarity0
Continuous 3D Label Stereo Matching using Local Expansion MovesCode0
Solving Dense Image Matching in Real-Time using Discrete-Continuous Optimization0
A Closed-Form Solution to Tensor Voting: Theory and Applications0
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