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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 8190 of 517 papers

TitleStatusHype
Digging Into Uncertainty-based Pseudo-label for Robust Stereo MatchingCode1
ELFNet: Evidential Local-global Fusion for Stereo MatchingCode1
Disparity Estimation Using a Quad-Pixel SensorCode1
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow EstimationCode1
CFNet: Cascade and Fused Cost Volume for Robust Stereo MatchingCode1
Learning Signed Distance Field for Multi-view Surface ReconstructionCode1
LiDAR-Event Stereo Fusion with HallucinationsCode1
Diving into the Fusion of Monocular Priors for Generalized Stereo MatchingCode1
BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image MatchingCode1
Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume ExcitationCode1
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