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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 381–390 of 517 papers

TitleStatusHype
Content-Aware Inter-Scale Cost Aggregation for Stereo Matching—0
Continuous Cost Aggregation for Dual-Pixel Disparity Extraction—0
Correcting Decalibration of Stereo Cameras in Self-Driving Vehicles—0
Co-Teaching: An Ark to Unsupervised Stereo Matching—0
Cross-Modality 3D Object Detection—0
CV-HAZOP: Introducing Test Data Validation for Computer Vision—0
Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs—0
Deep Learning of Partial Graph Matching via Differentiable Top-K—0
Deep Material-Aware Cross-Spectral Stereo Matching—0
Deep Stereo Matching with Dense CRF Priors—0
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