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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 201–210 of 517 papers

TitleStatusHype
Geometry-Aware Unsupervised Domain Adaptation for Stereo Matching—0
Geometry-based Occlusion-Aware Unsupervised Stereo Matching for Autonomous Driving—0
CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching—0
Instance-aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning—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
Instantaneous Stereo Depth Estimation of Real-World Stimuli with a Neuromorphic Stereo-Vision Setup—0
Epipolar Geometry Based On Line Similarity—0
Gromov-Wasserstein Problem with Cyclic Symmetry—0
Entropy-difference based stereo error detection—0
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