Author:
Sai Koneru Pranav,Pande Sagar Dhanraj
Abstract
INTRODUCTION: The division of a 3D point cloud into various meaningful regions or objects is known as point cloud segmentation.
OBJECTIVES: The paper discusses the challenges faced in 3D point cloud segmentation, such as the high dimensionality of point cloud data, noise, and varying point densities.
METHODS: The paper compares several commonly used datasets in the field, including the ModelNet, ScanNet, S3DIS, and Semantic 3D datasets, ApploloCar3D, and provides an analysis of the strengths and weaknesses of each dataset. Also provides an overview of the papers that uses Traditional clustering techniques, deep learning-based methods, and hybrid approaches in point cloud semantic segmentation. The report also discusses the benefits and drawbacks of each approach.
CONCLUSION: This study sheds light on the state of the art in semantic segmentation of 3D point clouds.
Publisher
European Alliance for Innovation n.o.
Subject
General Chemical Engineering
Reference39 articles.
1. Chen, X.. Ma, H.. Wan, J.. Li, B.. Xia, T.: Multi-view 3D object detection network for au- tonomous driving. CVPR, (2017).
2. Juana, V. H., Abhinav, V.: Chapter 12 - Semantic scene segmentation for robotics, Editor(s): Alexandros Iosifidis, Anastasios Tefas, Deep Learning for Robot Perception and Cognition. Academic Press, 279-311, (2022).
3. Huang, X., Mei, G., Zhang, J., Abbas, R.: A comprehensive survey on point cloud registra- tion, arXiv:2103.02690, (2021).
4. Guo, Y., Wang, H., Hu, Q., Liu, H., Liu, L., Bennamoun, M.: Deep learning for 3D point clouds: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 43(12), 4338–4364 (2021).
5. Lu, H., Shi, H.: Deep learning for 3D point cloud understanding: A survey. arXiv:2009.08920, (2020).
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献