Analysis of Current Advancement in 3D Point Cloud Semantic Segmentation

EAI Endorsed Transactions on Internet of Things Pub Date : 2023-11-28 DOI:10.4108/eetiot.4495

Koneru Pranav Sai, Sagar Dhanaraj Pande

引用次数: 0

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.

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三维点云语义分割的最新进展分析

简介：将三维点云划分为各种有意义的区域或对象称为点云分割。目标：本文讨论了三维点云分割所面临的挑战，如点云数据的高维性、噪声和不同的点密度。方法：本文比较了该领域常用的几个数据集，包括 ModelNet、ScanNet、S3DIS 和 Semantic 3D 数据集 ApploloCar3D，并对每个数据集的优缺点进行了分析。报告还概述了在点云语义分割中使用传统聚类技术、基于深度学习的方法和混合方法的论文。报告还讨论了每种方法的优点和缺点。结论：本研究揭示了三维点云语义分割的技术现状。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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EAI Endorsed Transactions on Internet of Things

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