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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 321–330 of 517 papers

TitleStatusHype
WaveletStereo: Learning Wavelet Coefficients of Disparity Map in Stereo Matching—0
Wide baseline stereo matching with convex bounded-distortion constraints—0
Wide Baseline Stereo Matching With Convex Bounded Distortion Constraints—0
Widening siamese architectures for stereo matching—0
NLCA-Net v2 for Stereo Matching in ECCV'20 Robust Vision Challenge—0
MSDC-Net: Multi-Scale Dense and Contextual Networks for Automated Disparity Map for Stereo Matching—0
Fully Parallel Architecture for Semi-global Stereo Matching with Refined Rank Method—0
DISCO: Depth Inference from Stereo using Context—0
MCPDepth: Omnidirectional Depth Estimation via Stereo Matching from Multi-Cylindrical Panoramas—0
Gaussian Mixture based Evidential Learning for Stereo Matching—0
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