Guest editorial: Deep learning-based point cloud processing, compression and analysis
Research output: Journal Publications and Reviews › Editorial Preface
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Article number | e13266 |
Journal / Publication | Electronics Letters |
Volume | 60 |
Issue number | 14 |
Online published | 12 Jul 2024 |
Publication status | Published - Jul 2024 |
Link(s)
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.
In: Electronics Letters, Vol. 60, No. 14, e13266, 07.2024.
Research output: Journal Publications and Reviews › Editorial Preface