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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

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Showing 171–180 of 517 papers

TitleStatusHype
All-in-One: Transferring Vision Foundation Models into Stereo Matching—0
A Comparative Study on Deep-Learning Methods for Dense Image Matching of Multi-angle and Multi-date Remote Sensing Stereo Images—0
Comparison of Stereo Matching Algorithms for the Development of Disparity Map—0
3D Reconstruction of Curvilinear Structures with Stereo Matching DeepConvolutional Neural Networks—0
Geometry-Aware Unsupervised Domain Adaptation for Stereo Matching—0
A Learning-based Framework for Hybrid Depth-from-Defocus and Stereo Matching—0
Event-Driven Stereo Matching for Real-Time 3D Panoramic Vision—0
CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching—0
Genetic Stereo Matching Algorithm with Fuzzy Fitness—0
A Learned Stereo Depth System for Robotic Manipulation in Homes—0
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