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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 351–360 of 517 papers

TitleStatusHype
A Disparity Refinement Framework for Learning-based Stereo Matching Methods in Cross-domain Setting for Laparoscopic Images—0
Advancing Applications of Satellite Photogrammetry: Novel Approaches for Built-up Area Modeling and Natural Environment Monitoring using Stereo/Multi-view Satellite Image-derived 3D Data—0
A Learned Stereo Depth System for Robotic Manipulation in Homes—0
A Learning-based Framework for Hybrid Depth-from-Defocus and Stereo Matching—0
All-in-One: Transferring Vision Foundation Models into Stereo Matching—0
AMNet: Deep Atrous Multiscale Stereo Disparity Estimation Networks—0
Analysis of critical parameters of satellite stereo image for 3D reconstruction and mapping—0
Analysis of different disparity estimation techniques on aerial stereo image datasets—0
Analyzing Computer Vision Data - The Good, the Bad and the Ugly—0
A Nearest Neighbor Network to Extract Digital Terrain Models from 3D Point Clouds—0
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