Keyframe image processing of semantic 3D point clouds based on deep learning

With the rapid development of web technologies and the popularity of smartphones, users are uploading and sharing a large number of images every day. Therefore, it is a very important issue nowadays to enable users to discover exactly the information they need in the vast amount of data and to make...

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Bibliographic Details
Published in:Frontiers in neurorobotics 2023-01, Vol.16, p.988024-988024
Main Authors: Wang, Junxian, Lv, Wei, Wang, Zhouya, Zhang, Xiaolong, Jiang, Meixuan, Gao, Junhan, Chen, Shangwen
Format: Article
Language:English
Subjects:
3D point cloud
Clustering
Data analysis
Deep learning
Image processing
Industrial design
Information processing
Iterative methods
keyframe
Machine learning
Methods
Neural networks
Neuroscience
Segmentation
Semantics
Signal processing
Simulation
U-Net
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Summary:With the rapid development of web technologies and the popularity of smartphones, users are uploading and sharing a large number of images every day. Therefore, it is a very important issue nowadays to enable users to discover exactly the information they need in the vast amount of data and to make it possible to integrate their large amount of image material efficiently. However, traditional content-based image retrieval techniques are based on images, and there is a "semantic gap" between this and people's understanding of images. To address this "semantic gap," a keyframe image processing method for 3D point clouds is proposed, and based on this, a U-Net-based binary data stream semantic segmentation network is established for keyframe image processing of 3D point clouds in combination with deep learning techniques.
ISSN:1662-5218
1662-5218
DOI:10.3389/fnbot.2022.988024