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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 221–230 of 517 papers

TitleStatusHype
Continuous Cost Aggregation for Dual-Pixel Disparity Extraction—0
StereoVAE: A lightweight stereo-matching system using embedded GPUs—0
Visibility-Aware Pixelwise View Selection for Multi-View Stereo Matching—0
A Disparity Refinement Framework for Learning-based Stereo Matching Methods in Cross-domain Setting for Laparoscopic Images—0
Inter-View Depth Consistency Testing in Depth Difference Subspace—0
Parameterized Cost Volume for Stereo Matching—0
Deep Learning of Partial Graph Matching via Differentiable Top-K—0
High-Frequency Stereo Matching Network—0
Masked Representation Learning for Domain Generalized Stereo Matching—0
Learning Adaptive Dense Event Stereo From the Image Domain—0
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