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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 261270 of 517 papers

TitleStatusHype
Cross-Modality 3D Object Detection0
CV-HAZOP: Introducing Test Data Validation for Computer Vision0
Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs0
Deep Learning of Partial Graph Matching via Differentiable Top-K0
Deep Material-Aware Cross-Spectral Stereo Matching0
Deep Stereo Matching with Dense CRF Priors0
Deep Stereo Matching with Explicit Cost Aggregation Sub-Architecture0
Degradation-agnostic Correspondence from Resolution-asymmetric Stereo0
Dense 3D Reconstruction Through Lidar: A Comparative Study on Ex-vivo Porcine Tissue0
Dedge-AGMNet:an effective stereo matching network optimized by depth edge auxiliary task0
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