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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 491–500 of 517 papers

TitleStatusHype
An Adversarial Generative Network Designed for High-Resolution Monocular Depth Estimation from 2D HiRISE Images of MarsCode0
Single View Stereo MatchingCode0
Real-Time Variational Fisheye Stereo without Rectification and UndistortionCode0
Adaptive Unimodal Cost Volume Filtering for Deep Stereo MatchingCode0
Distilling Stereo Networks for Performant and Efficient Leaner NetworksCode0
Hierarchical Discrete Distribution Decomposition for Match Density EstimationCode0
Hierarchical Deep Stereo Matching on High-resolution ImagesCode0
SOCRATES: A Stereo Camera Trap for Monitoring of BiodiversityCode0
Rethinking the Key Factors for the Generalization of Remote Sensing Stereo Matching NetworksCode0
CMD: Constraining Multimodal Distribution for Domain Adaptation in Stereo MatchingCode0
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