Self-supervised learning for point cloud data: A survey

Zeng, Changyu, Wang, Wei, Nguyen, Anh ORCID: 0000-0002-1449-211X and Yue, Yutao ORCID: 0000-0003-4532-0924 (2023) Self-supervised learning for point cloud data: A survey. Expert Systems with Applications, 237. p. 121354.

Text
2305.11881.pdf - Author Accepted Manuscript
Download (6MB) | Preview

Official URL: http://dx.doi.org/10.1016/j.eswa.2023.121354

Abstract

3D point clouds are a crucial type of data collected by LiDAR sensors and widely used in transportation applications due to its concise descriptions and accurate localization. Deep neural networks (DNNs) have achieved remarkable success in processing large amount of disordered and sparse 3D point clouds, especially in various computer vision tasks, such as pedestrian detection and vehicle recognition. Among all the learning paradigms, Self-Supervised Learning (SSL), an unsupervised training paradigm that mines effective information from the data itself, is considered as an essential solution to solve the time-consuming and labor-intensive data labeling problems via smart pre-training task design. This paper provides a comprehensive survey of recent advances on SSL for point clouds. We first present an innovative taxonomy, categorizing the existing SSL methods into four broad categories based on the pretexts’ characteristics. Under each category, we then further categorize the methods into more fine-grained groups and summarize the strength and limitations of the representative methods. We also compare the performance of the notable SSL methods in literature on multiple downstream tasks on benchmark datasets both quantitatively and qualitatively. Finally, we propose a number of future research directions based on the identified limitations of existing SSL research on point clouds.

Item Type:	Article
Uncontrolled Keywords:	Self-supervised learning, Computer vision, Point clouds, Representation learning, Pretext task, Transfer learning
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science Faculty of Science and Engineering > School of Physical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	04 Sep 2023 09:14
Last Modified:	28 Oct 2023 22:39
DOI:	10.1016/j.eswa.2023.121354
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3172515