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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 7180 of 517 papers

TitleStatusHype
ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching NetworksCode1
Hierarchical Neural Architecture Search for Deep Stereo MatchingCode1
Bilateral Grid Learning for Stereo Matching NetworksCode1
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo MatchingCode1
A Multi-spectral Dataset for Evaluating Motion Estimation SystemsCode1
Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view StereoCode1
BDIS-SLAM: A lightweight CPU-based dense stereo SLAM for surgeryCode1
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo MatchingCode1
Deep 3D Portrait from a Single ImageCode1
Context-Enhanced Stereo TransformerCode1
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