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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

Showing 461470 of 517 papers

TitleStatusHype
Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor ScenesCode0
Learning monocular depth estimation infusing traditional stereo knowledgeCode0
Zoom and Learn: Generalizing Deep Stereo Matching to Novel DomainsCode0
Uncertainty Quantification in Stereo MatchingCode0
Cross-Scale Cost Aggregation for Stereo MatchingCode0
3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume NormalizationCode0
Learning Monocular Depth by Distilling Cross-domain Stereo NetworksCode0
Continuous 3D Label Stereo Matching using Local Expansion MovesCode0
Continual Stereo Matching of Continuous Driving Scenes With Growing ArchitectureCode0
DSR: Direct Self-rectification for Uncalibrated Dual-lens CamerasCode0
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