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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 171180 of 517 papers

TitleStatusHype
Learning Inverse Depth Regression for Multi-View Stereo with Correlation Cost VolumeCode0
Binary Stereo MatchingCode0
3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume NormalizationCode0
Improved Stereo Matching with Constant Highway Networks and Reflective Confidence LearningCode0
Distilling Stereo Networks for Performant and Efficient Leaner NetworksCode0
Hierarchical Discrete Distribution Decomposition for Match Density EstimationCode0
Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgeryCode0
Hierarchical Deep Stereo Matching on High-resolution ImagesCode0
Digging Into Normal Incorporated Stereo MatchingCode0
Guided Stereo MatchingCode0
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