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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 291–300 of 517 papers

TitleStatusHype
High-precision target positioning system for unmanned vehicles based on binocular vision—0
Parallax Attention for Unsupervised Stereo Correspondence LearningCode1
Adjusting Bias in Long Range Stereo Matching: A semantics guided approach—0
End-to-End Deep Learning Model for Cardiac Cycle Synchronization from Multi-View Angiographic Sequences—0
Stereo Plane SLAM Based on Intersecting LinesCode1
Cross-Modality 3D Object Detection—0
Learning Stereo Matchability in Disparity Regression NetworksCode1
Learning Stereo from Single ImagesCode1
Stereo Event-based Particle Tracking Velocimetry for 3D Fluid Flow ReconstructionCode0
Du²Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels—0
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