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Guest Editorial: Deep Learning-based Point Cloud Processing, Compression and Analysis
  • +2
  • Yun Zhang,
  • Raouf Hamzaoui(GE),
  • Xu Wang,
  • Junhui Hou,
  • Giuseppe Valenzise(GE)
Yun Zhang
Sun Yat-Sen University

Corresponding Author:zhangyun2@mail.sysu.edu.cn

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Raouf Hamzaoui(GE)
De Montfort University
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Xu Wang
Shenzhen University
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Junhui Hou
City University of Hong Kong
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Giuseppe Valenzise(GE)
Universite Paris-Saclay
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Abstract

Point cloud data is a large collection of high dimensional 3D points with 3D coordinates and attributes, which has been one of the mainstream representations for emerging 3D applications, such as virtual reality, autonomous vehicles and robotics. Due to the large-scale unstructured high-dimensional nature of point clouds, point cloud processing, transmitting and analysing has been challenging issues in multimedia signal processing and communication. Deep learning is a powerful tool to learn statistical knowledge from massive data. Advances in artificial intelligence, especially deep learning models are offering new opportunities for point cloud processing, compression and analysis. This special issue aims at promoting cutting-edge research on deep learning-based point cloud processing, including object detection, segmentation, registration, compression, and visual quality assessment.
Submitted to Electronics Letters
15 Jun 2024Submitted to Electronics Letters
19 Jun 2024Submission Checks Completed
19 Jun 2024Assigned to Editor
19 Jun 2024Review(s) Completed, Editorial Evaluation Pending
19 Jun 2024Editorial Decision: Accept