  The introduction of inexpensive 3D data acquisition devices has promisingly
facilitated the wide availability and popularity of 3D point cloud, which
attracts more attention to the effective extraction of novel 3D point cloud
descriptors for accuracy of the efficiency of 3D computer vision tasks in
recent years. However, how to develop discriminative and robust feature
descriptors from 3D point cloud remains a challenging task due to their
intrinsic characteristics. In this paper, we give a comprehensively insightful
investigation of the existing 3D point cloud descriptors. These methods can
principally be divided into two categories according to the advancement of
descriptors: hand-crafted based and deep learning-based apporaches, which will
be further discussed from the perspective of elaborate classification, their
advantages, and limitations. Finally, we present the future research direction
of the extraction of 3D point cloud descriptors.
