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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 441–450 of 517 papers

TitleStatusHype
Segment Graph Based Image Filtering: Fast Structure-Preserving Smoothing—0
Segment-Tree Based Cost Aggregation for Stereo Matching—0
Select-and-Combine (SAC): A Novel Multi-Stereo Depth Fusion Algorithm for Point Cloud Generation via Efficient Local Markov Netlets—0
Self-Supervised Intensity-Event Stereo Matching—0
Self-Supervised Learning for Stereo Matching with Self-Improving Ability—0
Semantic See-Through Rendering on Light Fields—0
Semantic Stereo Matching With Pyramid Cost Volumes—0
Semi-dense Stereo Matching using Dual CNNs—0
Semi-Global Stereo Matching with Surface Orientation Priors—0
Semi-synthesis: A fast way to produce effective datasets for stereo matching—0
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