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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 491–500 of 517 papers

TitleStatusHype
Wide Baseline Stereo Matching With Convex Bounded Distortion Constraints—0
Fast Non-local Stereo Matching based on Hierarchical Disparity Prediction—0
Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives—0
Wide baseline stereo matching with convex bounded-distortion constraints—0
A Weighted Sparse Coding Framework for Saliency Detection—0
Leveraging Stereo Matching With Learning-Based Confidence Measures—0
Direction Matters: Depth Estimation With a Surface Normal Classifier—0
Event-Driven Stereo Matching for Real-Time 3D Panoramic Vision—0
Simultaneous Video Defogging and Stereo Reconstruction—0
Tracking Live Fish from Low-Contrast and Low-Frame-Rate Stereo Videos—0
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