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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

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Showing 491–500 of 517 papers

TitleStatusHype
Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks—0
Adjusting Bias in Long Range Stereo Matching: A semantics guided approach—0
Look Wider to Match Image Patches with Convolutional Neural Networks—0
LoS: Local Structure-Guided Stereo Matching—0
LSM: Learning Subspace Minimization for Low-level Vision—0
M3Depth: Wavelet-Enhanced Depth Estimation on Mars via Mutual Boosting of Dual-Modal Data—0
MaDis-Stereo: Enhanced Stereo Matching via Distilled Masked Image Modeling—0
MAP Disparity Estimation Using Hidden Markov Trees—0
Masked Representation Learning for Domain Generalized Stereo Matching—0
Match-Stereo-Videos: Bidirectional Alignment for Consistent Dynamic Stereo Matching—0
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