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Computer Science > Computer Vision and Pattern Recognition

arXiv:2405.11903 (cs)
[Submitted on 20 May 2024]

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

Authors:Sushmita Sarker, Prithul Sarker, Gunner Stone, Ryan Gorman, Alireza Tavakkoli, George Bebis, Javad Sattarvand
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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.
Comments: Published in Springer Nature (Machine Vision and Applications)
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2405.11903 [cs.CV]
  (or arXiv:2405.11903v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2405.11903
Journal reference: Machine Vision and Applications 35, 67 (2024)
Related DOI: https://doi.org/10.1007/s00138-024-01543-1

Submission history

From: Sushmita Sarker [view email]
[v1] Mon, 20 May 2024 09:33:27 UTC (2,900 KB)
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