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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
LightStereo: Channel Boost Is All Your Need for Efficient 2D Cost Aggregation—0
Epipolar Geometry Based On Line Similarity—0
Entropy-difference based stereo error detection—0
Deep Stereo Matching with Dense CRF Priors—0
Innovative 3D Depth Map Generation From A Holoscopic 3D Image Based on Graph Cut Technique—0
End-to-End Learning of Multi-scale Convolutional Neural Network for Stereo Matching—0
Instantaneous Stereo Depth Estimation of Real-World Stimuli with a Neuromorphic Stereo-Vision Setup—0
End-to-end Learning of Cost-Volume Aggregation for Real-time Dense Stereo—0
Inter-View Depth Consistency Testing in Depth Difference Subspace—0
Into the Twilight Zone: Depth Estimation using Joint Structure-Stereo Optimization—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
KCP: Kernel Cluster Pruning for Dense Labeling Neural Networks—0
End-to-End 3D Hand Pose Estimation from Stereo Cameras—0
Landmark Stereo Dataset for Landmark Recognition and Moving Node Localization in a Non-GPS Battlefield Environment—0
CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation—0
Learning Adaptive Dense Event Stereo From the Image Domain—0
Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives—0
Learning Dense Wide Baseline Stereo Matching for People—0
Depth Estimation Analysis of Orthogonally Divergent Fisheye Cameras with Distortion Removal—0
A Robust Real-Time Computing-based Environment Sensing System for Intelligent Vehicle—0
A Closed-Form Solution to Tensor Voting: Theory and Applications—0
MCPDepth: Omnidirectional Depth Estimation via Stereo Matching from Multi-Cylindrical Panoramas—0
CATS: A Color and Thermal Stereo Benchmark—0
Efficient High-Resolution Stereo Matching using Local Plane Sweeps—0
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