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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 91100 of 517 papers

TitleStatusHype
Matching-space Stereo Networks for Cross-domain GeneralizationCode1
FADNet: A Fast and Accurate Network for Disparity EstimationCode1
ES-Net: An Efficient Stereo Matching NetworkCode1
BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image MatchingCode1
Deep Laparoscopic Stereo Matching with TransformersCode1
DCVSMNet: Double Cost Volume Stereo Matching NetworkCode1
Deep 3D Portrait from a Single ImageCode1
Pyramid Stereo Matching NetworkCode1
Learning Signed Distance Field for Multi-view Surface ReconstructionCode1
Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume ExcitationCode1
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