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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 7180 of 517 papers

TitleStatusHype
GraftNet: Towards Domain Generalized Stereo Matching with a Broad-Spectrum and Task-Oriented FeatureCode1
Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency PerspectiveCode1
Depth Estimation by Combining Binocular Stereo and Monocular Structured-LightCode1
ChiTransformer:Towards Reliable Stereo from CuesCode1
Lightweight Multi-Drone Detection and 3D-Localization via YOLOCode1
ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching NetworksCode1
Discrete Time Convolution for Fast Event-Based StereoCode1
Stereoscopic Universal Perturbations across Different Architectures and DatasetsCode1
PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense ReconstructionCode1
TriStereoNet: A Trinocular Framework for Multi-baseline Disparity EstimationCode1
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