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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 211–220 of 517 papers

TitleStatusHype
Epipolar Geometry Based On Line Similarity—0
Entropy-difference based stereo error detection—0
End-to-End Learning of Multi-scale Convolutional Neural Network for Stereo Matching—0
CV-HAZOP: Introducing Test Data Validation for Computer Vision—0
End-to-end Learning of Cost-Volume Aggregation for Real-time Dense Stereo—0
3D Point Cloud Denoising using Graph Laplacian Regularization of a Low Dimensional Manifold Model—0
End-to-End Deep Learning Model for Cardiac Cycle Synchronization from Multi-View Angiographic Sequences—0
End-to-End 3D Hand Pose Estimation from Stereo Cameras—0
CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation—0
Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives—0
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