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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 151–160 of 517 papers

TitleStatusHype
Analyzing Computer Vision Data - The Good, the Bad and the Ugly—0
CV-HAZOP: Introducing Test Data Validation for Computer Vision—0
A Nearest Neighbor Network to Extract Digital Terrain Models from 3D Point Clouds—0
Cross-Modality 3D Object Detection—0
Co-Teaching: An Ark to Unsupervised Stereo Matching—0
Fast 3D Point Cloud Denoising via Bipartite Graph Approximation & Total Variation—0
Fast Multi-frame Stereo Scene Flow with Motion Segmentation—0
Analysis of different disparity estimation techniques on aerial stereo image datasets—0
Correcting Decalibration of Stereo Cameras in Self-Driving Vehicles—0
Continuous Cost Aggregation for Dual-Pixel Disparity Extraction—0
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