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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 141–150 of 517 papers

TitleStatusHype
Continuous 3D Label Stereo Matching using Local Expansion MovesCode0
Continual Stereo Matching of Continuous Driving Scenes With Growing ArchitectureCode0
An Adversarial Generative Network Designed for High-Resolution Monocular Depth Estimation from 2D HiRISE Images of MarsCode0
OmniMVS: End-to-End Learning for Omnidirectional Stereo MatchingCode0
Computing the Stereo Matching Cost with a Convolutional Neural NetworkCode0
A Lightweight Target-Driven Network of Stereo Matching for Inland WaterwaysCode0
Color Agnostic Cross-Spectral Disparity EstimationCode0
Learning Monocular Depth by Distilling Cross-domain Stereo NetworksCode0
Learning monocular depth estimation infusing traditional stereo knowledgeCode0
CMD: Constraining Multimodal Distribution for Domain Adaptation in Stereo MatchingCode0
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