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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 311320 of 517 papers

TitleStatusHype
Unsupervised Deep Asymmetric Stereo Matching With Spatially-Adaptive Self-Similarity0
Unsupervised Learning of Stereo Matching0
Unsupervised monocular stereo matching0
Using Orthophoto for Building Boundary Sharpening in the Digital Surface Model0
UWStereo: A Large Synthetic Dataset for Underwater Stereo Matching0
VHS: High-Resolution Iterative Stereo Matching with Visual Hull Priors0
Virtual Blood Vessels in Complex Background using Stereo X-ray Images0
Visibility-Aware Pixelwise View Selection for Multi-View Stereo Matching0
Visually Imbalanced Stereo Matching0
Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimation0
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