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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 91100 of 517 papers

TitleStatusHype
Generalized Correspondence Matching via Flexible Hierarchical Refinement and Patch Descriptor Distillation0
Selective-Stereo: Adaptive Frequency Information Selection for Stereo MatchingCode2
Learning Intra-view and Cross-view Geometric Knowledge for Stereo Matching0
CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation0
Digging Into Normal Incorporated Stereo MatchingCode0
DCVSMNet: Double Cost Volume Stereo Matching NetworkCode1
Landmark Stereo Dataset for Landmark Recognition and Moving Node Localization in a Non-GPS Battlefield Environment0
Depth-aware Volume Attention for Texture-less Stereo MatchingCode1
Icy Moon Surface Simulation and Stereo Depth Estimation for Sampling AutonomyCode0
Stereo-Matching Knowledge Distilled Monocular Depth Estimation Filtered by Multiple Disparity Consistency0
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