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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 141–150 of 517 papers

TitleStatusHype
Deep Stereo Matching with Explicit Cost Aggregation Sub-Architecture—0
An underwater binocular stereo matching algorithm based on the best search domain—0
Deep Stereo Matching with Dense CRF Priors—0
A novel stereo matching pipeline with robustness and unfixed disparity search range—0
Deep Material-Aware Cross-Spectral Stereo Matching—0
Deep Learning of Partial Graph Matching via Differentiable Top-K—0
A Noncontact Technique for Wave Measurement Based on Thermal Stereography and Deep Learning—0
2T-UNET: A Two-Tower UNet with Depth Clues for Robust Stereo Depth Estimation—0
Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs—0
Analyzing Computer Vision Data - The Good, the Bad and the Ugly—0
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