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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 431440 of 517 papers

TitleStatusHype
EdgeStereo: A Context Integrated Residual Pyramid Network for Stereo Matching0
Single View Stereo MatchingCode0
Real-Time Dense Stereo Matching With ELAS on FPGA Accelerated Embedded DevicesCode0
Deep Stereo Matching with Explicit Cost Aggregation Sub-Architecture0
Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network0
SOS: Stereo Matching in O(1) with Slanted Support WindowsCode1
Learning for Disparity Estimation through Feature ConstancyCode0
Semi-Global Stereo Matching with Surface Orientation Priors0
Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs0
Entropy-difference based stereo error detection0
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