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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 151–175 of 517 papers

TitleStatusHype
Analyzing Computer Vision Data - The Good, the Bad and the Ugly—0
CV-HAZOP: Introducing Test Data Validation for Computer Vision—0
A Nearest Neighbor Network to Extract Digital Terrain Models from 3D Point Clouds—0
Cross-Modality 3D Object Detection—0
Co-Teaching: An Ark to Unsupervised Stereo Matching—0
Geometry-based Occlusion-Aware Unsupervised Stereo Matching for Autonomous Driving—0
Analysis of different disparity estimation techniques on aerial stereo image datasets—0
Correcting Decalibration of Stereo Cameras in Self-Driving Vehicles—0
Continuous Cost Aggregation for Dual-Pixel Disparity Extraction—0
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
Geometry-Aware Unsupervised Domain Adaptation for Stereo Matching—0
Ghost-Stereo: GhostNet-based Cost Volume Enhancement and Aggregation for Stereo Matching Networks—0
Consistency-aware Self-Training for Iterative-based Stereo Matching—0
Confidence Inference for Focused Learning in Stereo Matching—0
All-in-One: Transferring Vision Foundation Models into Stereo Matching—0
A Comparative Study on Deep-Learning Methods for Dense Image Matching of Multi-angle and Multi-date Remote Sensing Stereo Images—0
Comparison of Stereo Matching Algorithms for the Development of Disparity Map—0
3D Reconstruction of Curvilinear Structures with Stereo Matching DeepConvolutional Neural Networks—0
A Learning-based Framework for Hybrid Depth-from-Defocus and Stereo Matching—0
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