IEEE Transactions on Pattern Analysis and Machine Intelligence· 2020Q1· Review
Deep Learning for 3D Point Clouds: A Survey
- 2,272citations
- Q1SCImago
- 2020year
Short summary
This paper surveys recent deep learning methods for 3D point cloud processing, covering classification, object detection/tracking, and segmentation.
AI-generated from the title and abstract; the full text is not read.
Key points
- Deep learning for 3D point clouds is a rapidly growing but challenging area.
- Key applications include 3D shape classification, object detection/tracking, and segmentation.
- The survey covers recent progress and presents comparative results on public datasets.
- It offers observations and suggests future research directions.
AI-generated from the title and abstract; the full text is not read.
Abstract
Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems. However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks. Recently, deep learning on point clouds has become even thriving, with numerous methods being proposed to address different problems in this area. To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds. It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions.
The authors' abstract, as published at the source. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 · DOI ↗
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Field: Computational Mechanics
Computational MechanicsEngineering