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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 161–170 of 517 papers

TitleStatusHype
Analysis of critical parameters of satellite stereo image for 3D reconstruction and mapping—0
3D Scene Geometry Estimation from 360^ Imagery: A Survey—0
Gaussian Mixture based Evidential Learning for Stereo Matching—0
A Comparison of Stereo-Matching Cost between Convolutional Neural Network and Census for Satellite Images—0
Fully Parallel Architecture for Semi-global Stereo Matching with Refined Rank Method—0
Content-Aware Inter-Scale Cost Aggregation for Stereo Matching—0
AMNet: Deep Atrous Multiscale Stereo Disparity Estimation Networks—0
FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications—0
Genetic Stereo Matching Algorithm with Fuzzy Fitness—0
Geometry-based Occlusion-Aware Unsupervised Stereo Matching for Autonomous Driving—0
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