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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 151160 of 517 papers

TitleStatusHype
Mono2Stereo: Monocular Knowledge Transfer for Enhanced Stereo Matching0
These Maps Are Made by Propagation: Adapting Deep Stereo Networks to Road Scenarios with Decisive Disparity Diffusion0
Adaptive Stereo Depth Estimation with Multi-Spectral Images Across All Lighting Conditions0
Segmentation-aware Prior Assisted Joint Global Information Aggregated 3D Building Reconstruction0
A Lightweight Target-Driven Network of Stereo Matching for Inland WaterwaysCode0
Analysis of different disparity estimation techniques on aerial stereo image datasets0
The Sampling-Gaussian for stereo matching0
Match Stereo Videos via Bidirectional Alignment0
Robust and Flexible Omnidirectional Depth Estimation with Multiple 360° Cameras0
MaDis-Stereo: Enhanced Stereo Matching via Distilled Masked Image Modeling0
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