Table 1.

Related works on surveys of point clouds and their application.

Reference	Main Contents
Nygren et al. 2016 [26]	The traditional algorithms for 3D point cloud segmentation and classification
Nguyen et al. 2013 [15]	The segmentation methods for the 3D point cloud.
Ahmed et al. 2018 [16]	The 3D data from Euclidean and the non-Euclidean geometry and a discussion on how to apply deep learning to the 3D dataset.
Hana et al. 2018 [9]	The feature descriptors of point clouds with three classes, i.e., local-based, global-based, and hybrid-based.
Garcia et al. 2017 [40]	The semantic segmentation methods based on deep learning.
Bronstein et al. 2017 [41]	The problems of geometric deep learning, extending grid-like deep learning methods to non-Euclidean structures.
Griffiths et al. 2019 [42]	The classification models for processing 3D unstructured Euclidean data.