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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
Bilateral Grid Learning for Stereo Matching NetworksCode1
Disparity Estimation Using a Quad-Pixel SensorCode1
A Multi-spectral Dataset for Evaluating Motion Estimation SystemsCode1
BDIS-SLAM: A lightweight CPU-based dense stereo SLAM for surgeryCode1
CFNet: Cascade and Fused Cost Volume for Robust Stereo MatchingCode1
Diving into the Fusion of Monocular Priors for Generalized Stereo MatchingCode1
ELFNet: Evidential Local-global Fusion for Stereo MatchingCode1
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo MatchingCode1
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow EstimationCode1
ES-Net: An Efficient Stereo Matching NetworkCode1
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