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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 3140 of 517 papers

TitleStatusHype
Practical Stereo Matching via Cascaded Recurrent Network with Adaptive CorrelationCode2
Attention Concatenation Volume for Accurate and Efficient Stereo MatchingCode2
QuadTree Attention for Vision TransformersCode2
RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo MatchingCode2
Diving into the Fusion of Monocular Priors for Generalized Stereo MatchingCode1
Multi-Label Stereo Matching for Transparent Scene Depth EstimationCode1
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation ModelCode1
LightEndoStereo: A Real-time Lightweight Stereo Matching Method for Endoscopy ImagesCode1
Hadamard Attention Recurrent Transformer: A Strong Baseline for Stereo Matching TransformerCode1
OmniStereo: Real-time Omnidireactional Depth Estimation with Multiview Fisheye CamerasCode1
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