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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 226–250 of 517 papers

TitleStatusHype
H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometry—0
High-Resolution Depth Maps Based on TOF-Stereo Fusion—0
High-precision target positioning system for unmanned vehicles based on binocular vision—0
High-Frequency Stereo Matching Network—0
Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs—0
HeightFormer: A Multilevel Interaction and Image-adaptive Classification-regression Network for Monocular Height Estimation with Aerial Images—0
Instantaneous Stereo Depth Estimation of Real-World Stimuli with a Neuromorphic Stereo-Vision Setup—0
CV-HAZOP: Introducing Test Data Validation for Computer Vision—0
Inter-View Depth Consistency Testing in Depth Difference Subspace—0
Into the Twilight Zone: Depth Estimation using Joint Structure-Stereo Optimization—0
Gromov-Wasserstein Problem with Cyclic Symmetry—0
Cross-Modality 3D Object Detection—0
KCP: Kernel Cluster Pruning for Dense Labeling Neural Networks—0
Graph Cut based Continuous Stereo Matching using Locally Shared Labels—0
Co-Teaching: An Ark to Unsupervised Stereo Matching—0
A Nearest Neighbor Network to Extract Digital Terrain Models from 3D Point Clouds—0
Learning Adaptive Dense Event Stereo From the Image Domain—0
Analyzing Computer Vision Data - The Good, the Bad and the Ugly—0
Learning Dense Wide Baseline Stereo Matching for People—0
Gradient-based Camera Exposure Control for Outdoor Mobile Platforms—0
Ghost-Stereo: GhostNet-based Cost Volume Enhancement and Aggregation for Stereo Matching Networks—0
Analysis of different disparity estimation techniques on aerial stereo image datasets—0
Geometry-based Occlusion-Aware Unsupervised Stereo Matching for Autonomous Driving—0
Geometry-Aware Unsupervised Domain Adaptation for Stereo Matching—0
Correcting Decalibration of Stereo Cameras in Self-Driving Vehicles—0
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