几何特征与神经网络联合优化的室内三维点云语义分割方法

doi:10.13474/j.cnki.11-2246.2023.0136

测绘通报 ›› 2023, Vol. 0 ›› Issue (5): 56-61.doi: 10.13474/j.cnki.11-2246.2023.0136

几何特征与神经网络联合优化的室内三维点云语义分割方法

姚萌萌^1,2,3, 李晓明^1,2,3, 王伟玺^1,2,3, 谢林甫^1,2,3, 黄俊杰^1,2,3, 黄鸿盛^1,2,3, 汤圣君^1,2,3

1. 深圳大学建筑与城市规划学院智慧城市研究院, 广东深圳 518061;
2. 自然资源部城市国土资源监测与仿真重点实验室, 广东深圳 518061;
3. 粤港澳智慧城市联合实验室, 广东深圳 518061

收稿日期:2022-06-15 发布日期:2023-05-31
通讯作者: 汤圣君。E-mail:shengjuntang@szu.edu.cn
作者简介:姚萌萌(1996-),男,硕士,主要研究方向为三维点云分类分割。E-mail:yaomeng_1996@163.com
基金资助:
深圳市科技计划面上项目（JCYJ20210324093012033）；国家自然科学基金青年基金（41801392）；广东省自然科学基金面上项目（2121A1515012574）；自然资源部城市自然资源监测与仿真重点实验室开放基金（KF-2021-06-125）

Semantic segmentation of indoor 3D point cloud by joint optimization of geometric features and neural networks

YAO Mengmeng^1,2,3, LI Xiaoming^1,2,3, WANG Weixi^1,2,3, XIE Linfu^1,2,3, HUANG Junjie^1,2,3, HUANG Hongsheng^1,2,3, TANG Shengjun^1,2,3

1. Institute of Smart City, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518061, China;
2. Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518061, China;
3. Guangdong-Hong Kong-Macau Joint Laboratory for Smart Citses, Shenzhen 518061, China

Received:2022-06-15 Published:2023-05-31

摘要/Abstract

摘要： 室内三维点云数据精准语义分割是实现深层次室内空间应用的基础。针对现有三维点云数据语义分割方法存在目标不完整和不一致的问题，本文提出了一种几何特征与深度神经网络联合优化的室内三维点云语义分割方法。该方法首先利用深度学习实现室内结构信息语义标签的初步提取，然后利用几何与颜色特征的点云分割方法对原始数据进行精确分割，最后利用概率模型将深度学习语义分割结果与几何分割结果进行交叉融合，实现语义分割结果的联合优化。基于开放数据集对本文提出的分割方法进行了精度和有效性验证，分别采用室内场景简单到复杂的三组室内点云数据进行了测试，试验结果表明，本文提出的方法能够有效提升室内三维点云语义分割精度。

关键词: 神经网络, 点云, 语义分割, 多级平面提取, 颜色区域增长分割

Abstract: A precise semantic segmentation of indoor 3D point cloud is the basis for realizing deep applications of interior space. To address the problem of incomplete and inconsistent segmentation objectives of existing semantic segmentation methods for 3D point clouds. In this paper, an semantic segmentation method for point clouds is proposed, it uses geometric features of point clouds and deep neural networks. First of all, it uses deep learning to achieve the initial extraction of semantic labels of indoor structural information. Secondly, it uses the segmentation method of point cloud with geometric features and color features to accurately segment the original data.Finally, a probabilistic model has proposed to cross-validate the initial segmentation results with the segmentation results of geometric features to achieve joint optimization of the results for semantic segmentation. The accuracy and validity of the segmentation method proposed in this paper are verified based on open-source datasets, and three sets of indoor point cloud data from simple to complex indoor scenes are tested respectively, and the experimental results show that the method proposed in this paper can effectively improve the semantic segmentation accuracy of the indoor 3D point cloud.

Key words: neural network, point cloud, semantic segmentation, multi-level plane extraction, color region segmentation

中图分类号:

P237

姚萌萌, 李晓明, 王伟玺, 谢林甫, 黄俊杰, 黄鸿盛, 汤圣君. 几何特征与神经网络联合优化的室内三维点云语义分割方法[J]. 测绘通报, 2023, 0(5): 56-61.

YAO Mengmeng, LI Xiaoming, WANG Weixi, XIE Linfu, HUANG Junjie, HUANG Hongsheng, TANG Shengjun. Semantic segmentation of indoor 3D point cloud by joint optimization of geometric features and neural networks[J]. Bulletin of Surveying and Mapping, 2023, 0(5): 56-61.

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几何特征与神经网络联合优化的室内三维点云语义分割方法

Semantic segmentation of indoor 3D point cloud by joint optimization of geometric features and neural networks

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