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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 201–225 of 517 papers

TitleStatusHype
Geometry-Aware Unsupervised Domain Adaptation for Stereo Matching—0
Geometry-based Occlusion-Aware Unsupervised Stereo Matching for Autonomous Driving—0
Expanding Sparse Guidance for Stereo Matching—0
Event-Driven Stereo Matching for Real-Time 3D Panoramic Vision—0
CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching—0
Learning Dense Stereo Matching for Digital Surface Models from Satellite Imagery—0
A Learned Stereo Depth System for Robotic Manipulation in Homes—0
A Comparative Evaluation of SGM Variants (including a New Variant, tMGM) for Dense Stereo Matching—0
Gromov-Wasserstein Problem with Cyclic Symmetry—0
Learning Dense Wide Baseline Stereo Matching for People—0
Learning Residual Flow as Dynamic Motion from Stereo Videos—0
Epipolar Geometry Based On Line Similarity—0
HeightFormer: A Multilevel Interaction and Image-adaptive Classification-regression Network for Monocular Height Estimation with Aerial Images—0
CV-HAZOP: Introducing Test Data Validation for Computer Vision—0
Entropy-difference based stereo error detection—0
End-to-End Learning of Multi-scale Convolutional Neural Network for Stereo Matching—0
End-to-end Learning of Cost-Volume Aggregation for Real-time Dense Stereo—0
High-precision target positioning system for unmanned vehicles based on binocular vision—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
HyperDepth: Learning Depth From Structured Light Without Matching—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
A Closed-Form Solution to Tensor Voting: Theory and Applications—0
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