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A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation

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Abstract

Point cloud analysis has a wide range of applications in many areas such as computer vision, robotic manipulation, and autonomous driving. While deep learning has achieved remarkable success on image-based tasks, there are many unique challenges faced by deep neural networks in processing massive, unordered, irregular and noisy 3D points. To stimulate future research, this paper analyzes recent progress in deep learning methods employed for point cloud processing and presents challenges and potential directions to advance this field. It serves as a comprehensive review on two major tasks in 3D point cloud processing—namely, 3D shape classification and semantic segmentation.

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Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant No. OIA-2148788.

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  1. Department of Computer Science and Engineering, University of Nevada, Reno, NV, USA

    Sushmita Sarker, Prithul Sarker, Gunner Stone, Ryan Gorman, Alireza Tavakkoli & George Bebis

  2. Department of Mining and Metallurgical Engineering, University of Nevada, Reno, NV, USA

    Javad Sattarvand

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Sushmita Sarker authored the main manuscript text. Gunner Stone generated the figures (Fig. 6). Prithul Sarker, Gunner Stone, and Ryan Gorman contributed materials, and verified the accuracy of all information. The manuscript was collectively reviewed by all authors.

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Sarker, S., Sarker, P., Stone, G. et al. A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation. Machine Vision and Applications 35, 67 (2024). https://doi.org/10.1007/s00138-024-01543-1

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