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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 421–430 of 517 papers

TitleStatusHype
Rectified Iterative Disparity for Stereo Matching—0
Rethinking Iterative Stereo Matching from Diffusion Bridge Model Perspective—0
Revealing the Reciprocal Relations Between Self-Supervised Stereo and Monocular Depth Estimation—0
RGB-Multispectral Matching: Dataset, Learning Methodology, Evaluation—0
RGB-Phase Speckle: Cross-Scene Stereo 3D Reconstruction via Wrapped Pre-Normalization—0
Robust and accurate depth estimation by fusing LiDAR and Stereo—0
Robust and Flexible Omnidirectional Depth Estimation with Multiple 360° Cameras—0
Robust Depth Estimation from Auto Bracketed Images—0
RobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather—0
Robust Full-FoV Depth Estimation in Tele-wide Camera System—0
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