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3D point cloud descriptors: state-of-the-art

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Abstract

The development of inexpensive 3D data acquisition devices has promisingly facilitated the wide availability and popularity of point clouds, which attracts increasing attention to the effective extraction of 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 representations from 3D point clouds 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 be principally divided into two categories according to their advancement: hand-crafted and deep learning-based approaches, which will be further discussed from the perspective of elaborate classification, their advantages, and limitations. Finally, we present the future research directions of the extraction of 3D point cloud descriptors.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (No. 62002299), and the Natural Science Foundation of Chongqing of China (No. cstc2020jcyj-msxmX0126), and the Fundamental Research Funds for the Central Universities (No. SWU120005).

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    Xian-Feng Han, Zhi-Ao Feng & Guo-Qiang Xiao

  2. Chang’an University, Xi’an, 710064, Shaanxi, China

    Shi-Jie Sun

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Han, XF., Feng, ZA., Sun, SJ. et al. 3D point cloud descriptors: state-of-the-art. Artif Intell Rev 56, 12033–12083 (2023). https://doi.org/10.1007/s10462-023-10486-4

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