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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 101110 of 517 papers

TitleStatusHype
Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume ExcitationCode1
Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view StereoCode1
Disparity Estimation Using a Quad-Pixel SensorCode1
Rational Polynomial Camera Model Warping for Deep Learning Based Satellite Multi-View Stereo MatchingCode1
3D Surface Reconstruction From Multi-Date Satellite ImagesCode1
Adaptive Multi-Modal Cross-Entropy Loss for Stereo MatchingCode1
Discrete Time Convolution for Fast Event-Based StereoCode1
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow EstimationCode1
Digging Into Uncertainty-based Pseudo-label for Robust Stereo MatchingCode1
Diving into the Fusion of Monocular Priors for Generalized Stereo MatchingCode1
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