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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

Showing 241250 of 517 papers

TitleStatusHype
CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation0
Learning Adaptive Dense Event Stereo From the Image Domain0
Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives0
Learning Dense Wide Baseline Stereo Matching for People0
Depth Estimation Analysis of Orthogonally Divergent Fisheye Cameras with Distortion Removal0
A Robust Real-Time Computing-based Environment Sensing System for Intelligent Vehicle0
A Closed-Form Solution to Tensor Voting: Theory and Applications0
MCPDepth: Omnidirectional Depth Estimation via Stereo Matching from Multi-Cylindrical Panoramas0
CATS: A Color and Thermal Stereo Benchmark0
Efficient High-Resolution Stereo Matching using Local Plane Sweeps0
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