Guest editorial: Deep learning-based point cloud processing, compression and analysis

Research output: Journal Publications and ReviewsEditorial Preface

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Author(s)

  • Yun Zhang
  • Raouf Hamzaoui
  • Xu Wang
  • Junhui Hou
  • Giuseppe Valenzise

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article numbere13266
Journal / PublicationElectronics Letters
Volume60
Issue number14
Online published12 Jul 2024
Publication statusPublished - Jul 2024

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. © 2024 The Author(s). Electronics Letters published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

Research Area(s)

  • image coding, learning (artificial intelligence), multidimensional signal processing, object recognition, sampling methods, virtual reality

Citation Format(s)

Guest editorial: Deep learning-based point cloud processing, compression and analysis. / Zhang, Yun; Hamzaoui, Raouf; Wang, Xu et al.
In: Electronics Letters, Vol. 60, No. 14, e13266, 07.2024.

Research output: Journal Publications and ReviewsEditorial Preface