城市竣工测绘典型要素语义分割PointNet++深度学习模型适用性分析

doi:10.13474/j.cnki.11-2246.2024.0215

测绘通报 ›› 2024, Vol. 0 ›› Issue (2): 85-89.doi: 10.13474/j.cnki.11-2246.2024.0215

城市竣工测绘典型要素语义分割PointNet++深度学习模型适用性分析

黄应华¹, 董振川², 李昊³, 陈壮², 刘长睿¹, 张献州¹

1. 西南交通大学地球科学与环境工程学院, 四川成都 611756;
2. 成都市勘察测绘研究院, 四川成都 610081;
3. 中国铁路设计集团有限公司, 天津 300000

收稿日期:2023-05-10 出版日期:2024-02-25 发布日期:2024-03-12
作者简介:黄应华(1999—),男,硕士,研究方向为基于深度学习的三维点云场景语义分割。E-mail:timelovery@qq.com
基金资助:
四川省测绘地理信息学会科技开放基金(CCX202216);城市建设项目竣工测绘点云分类与特征信息提取(KY-B2-2022-001)

Analysis of the applicability of PointNet++ deep learning model for semantic segmentation of typical elements of urban as-built mapping

HUANG Yinghua¹, DONG Zhenchuan², LI Hao³, CHEN Zhuang², LIU Changrui¹, ZHANG Xianzhou¹

1. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China;
2. Chengdu Institute of Survey & Investigation, Chengdu 610081, China;
3. China Railway Design Group Corproratioin, Tianjin 300000, China

Received:2023-05-10 Online:2024-02-25 Published:2024-03-12

摘要/Abstract

摘要： 处理三维激光扫描仪获取的城市竣工测绘点云场景数据的传统方法存在较多局限性,无法适应信息化社会对产品高效处理的需求。基于此,本文分析了城市竣工测绘点云场景分类需求,研究了利用深度学习网络模型对点云场景进行自动化处理的方法。首先,对输入的城市竣工测绘数据进行预处理,以实现点云降采样、去噪、地面点与非地面点分割;然后,人工标注5个区域场景数据毫米级标签,进行数据增强;最后,测试PointNet++网络在城市竣工测绘点云场景下的语义分割性能和效果。测试结果表明,在少量样本下,PointNet++网络可以较好地实现城市竣工测绘点云场景的激光点云语义分割,总体mIoU达73.06%,能够满足城市竣工测绘点云语义自动化分割需求,为城市竣工测绘点云数据处理提供了新思路。

关键词: 城市竣工测绘点云场景, 语义分割, 深度学习, 模型适用性

Abstract: The traditional methods for processing urban completion mapping point cloud scene data obtained by 3D laser scanner have several limitations and cannot meet the demand for efficient processing of products in the information society. In this paper, we analyze the demand for classification of urban completion mapping point cloud scenes and study the automated processing of point cloud scenes using a deep learning network model. Firstly, we preprocess the input urban completion mapping data to achieve point cloud downsampling, denoising, and ground point and non-ground point segmentation. Secondly, manually labels five regional scenes with millimeter-level labels and performs data augmentation techniques. And finally tests the semantic segmentation performance and effect of the PointNet++ network in urban completion mapping point cloud scenes. The test results show that the PointNet++ network can achieve the semantic segmentation of laser point clouds in urban completion mapping point cloud scenes with a small number of samples, and the overall mIoU reaches 73.06%, meeting the demand for semantic automatic segmentation of urban completion mapping point clouds and offering a new approach to processing urban completion mapping point cloud data.

Key words: urban as-built mapping point cloud scenes, semantic segmentation, deep learning, model applicability

中图分类号:

黄应华, 董振川, 李昊, 陈壮, 刘长睿, 张献州. 城市竣工测绘典型要素语义分割PointNet++深度学习模型适用性分析[J]. 测绘通报, 2024, 0(2): 85-89.

HUANG Yinghua, DONG Zhenchuan, LI Hao, CHEN Zhuang, LIU Changrui, ZHANG Xianzhou. Analysis of the applicability of PointNet++ deep learning model for semantic segmentation of typical elements of urban as-built mapping[J]. Bulletin of Surveying and Mapping, 2024, 0(2): 85-89.

参考文献

[1] 成都市工程建设项目“多测合一”技术细则(试行)[EB/OL].[2023-04-10].http://mpnr.chengdu.gov.cn/ghhzrzyj/sjwj/2019-12/20/fec7f6be51384dbab9d004533055be44/files/dcab4f283d3a41d189cfb36c82cdc81e.pdf.
[2] 赵若鹏.测绘新技术在建筑工程规划竣工测量中的应用研究[D].武汉: 湖北工业大学, 2018.
[3] 尚金光,张小波,陈超,等.三维激光扫描点云及其全景技术在“多测合一”中的应用[J].城市勘测,2020(2): 57-61.
[4] 倪福泽.加权总体最小二乘效率优化算法及其在测绘数据处理中的应用[D].太原: 太原理工大学,2020.
[5] 王峰,林鸿,李长辉.地面三维激光扫描技术在城市测绘中的应用[J].测绘通报,2012(5): 47-49.
[6] 王文曦,李乐林.深度学习在点云分类中的研究综述[J].计算机工程与应用,2022,58(1): 26-40.
[7] BOULCH A,GUERRY J,LE SAUX B,et al.SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks[J].Computers ＆ Graphics,2018,71: 189-198.
[8] MATURANA D,SCHERER S.VoxNet: a 3D convolutional neural network for real-time object recognition[C]//Proceedings of 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Hamburg,Germany: IEEE,2015: 922-928.
[9] CHARLES R Q,HAO Su,MO Kaichun,et al.PointNet: deep learning on point sets for 3D classification and segmentation[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Honolulu: IEEE,2017: 77-85.
[10] QI C R,YI Li,SU Hao,et al.PointNet++: deep hierarchical feature learning on point sets in a metric space[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.New York: ACM Press,2017: 5105-5114.
[11] HU Qingyong,YANG Bo,XIE Linhai,et al.RandLA-net: efficient semantic segmentation of large-scale point clouds[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Seattle: IEEE,2020: 11105-11114.
[12] ZHOU Yin,TUZEL O.VoxelNet: end-to-end learning for point cloud based 3D object detection[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City: IEEE,2018: 4490-4499.
[13] 郭昌龙,夏振平,李超超,等.结合改进半径滤波和局部平面拟合的点云去噪算法[J/OL].激光与光电子学进展: 1-11.[2023-12-20].http://kns.cnki.net/kcms/detail/31.1690.tn.20230920.1045.016.html.
[14] ZHANG Wuming,QI Jianbo,WAN Peng,et al.An easy-to-use airborne LiDAR data filtering method based on cloth simulation[J].Remote Sensing,2016,8(6): 501.
[15] 贾洋,李升甫,周城宇,等.高速公路典型要素语义分割深度学习模型适用性分析[J].北京测绘,2022,36(10): 1365-1369.

城市竣工测绘典型要素语义分割PointNet++深度学习模型适用性分析

Analysis of the applicability of PointNet++ deep learning model for semantic segmentation of typical elements of urban as-built mapping

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