| 最小割与深度学习联合优化的室内粘连点云分割方法 |
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| 引用本文： | 钱建国,张宇琦,汤圣君,王伟玺,李晓明.最小割与深度学习联合优化的室内粘连点云分割方法[J].测绘通报,2022,0(9):45-51. |
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| 作者姓名： | 钱建国 张宇琦 汤圣君 王伟玺 李晓明 |
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| 作者单位： | 1. 辽宁工程技术大学测绘与地理科学学院, 辽宁 阜新 123000;2. 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;3. 自然资源部城市自然资源监测与仿真重点实验室, 广东 深圳 518061;4. 深圳市空间信息智能感知与服务重点实验室, 广东 深圳 518061;5. 广东省城市空间信息工程重点实验室, 广东 深圳 518061 |
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| 基金项目： | 自然资源部城市自然资源监测与仿真重点实验室(KF-2019-04-010);国家自然科学基金(41801392;41901329;41971354;41971341) |
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| 摘 要： | 随着数字城市的发展,城市三维模型重建对三维点云结构化的需求与精度要求越来越高。如何有效准确地分割室内语义模型与三维重构是当前研究的热点问题。点云分割分类是室内点云结构化的重要基础,如何将粘连点云构件进行准确分割并用于室内点云结构化,是当前城市建模的难点。本文提出了一种面向室内粘连点云数据的分割分类方法。首先,利用深度学习网络处理室内点云数据;其次,对点云数据进行标签分类,得到目标标签点云;然后,利用欧氏算法对目标点云进行聚类分割,通过室内语义构件包围盒信息计算各目标中心点坐标与水平半径;最后,利用点云最小割实现室内粘连点云的准确分割。利用3组室内场景中获取的数据对分割方法的精度及有效性进行了验证。结果表明,该分割优化方法具有较高的精度与数据完整性。
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| 关 键 词： | 室内粘连点云 深度学习 标签点云分类 欧氏算法 最小割 |
| 收稿时间： | 2021-10-09 |
Indoor adherent point cloud segmentation method based on joint optimization of minimal cut and deep learning |
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| QIAN Jianguo,ZHANG Yuqi,TANG Shengjun,WANG Weixi,LI Xiaoming.Indoor adherent point cloud segmentation method based on joint optimization of minimal cut and deep learning[J].Bulletin of Surveying and Mapping,2022,0(9):45-51. |
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| Authors: | QIAN Jianguo ZHANG Yuqi TANG Shengjun WANG Weixi LI Xiaoming |
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| Abstract: | With the development of digital city, the demand for 3D point cloud structuring as well as the accuracy requirement of urban 3D model reconstruction is getting higher and higher. How to effectively and accurately segment indoor semantic models and 3D reconstruction is a current hot research issue. Point cloud segmentation classification is an important basis for indoor point cloud structuring, and how to segment the adherent point cloud components accurately and use them for indoor point cloud structuring is a difficult problem in current urban modeling. This paper proposes a segmentation and classification method for indoor adhesion point cloud data, which firstly uses deep learning network to process indoor point cloud data, then classifies the point cloud data with labeled point cloud to get the target labeled point cloud, and uses Euclidean algorithm to cluster and segment the target point cloud, calculates the coordinates of each target centroid and horizontal radius by the enclosing box information of indoor semantic components. Finally, we use point cloud minimization to achieve accurate segmentation of the indoor adherent point cloud. In this paper, three sets of data obtained from indoor scenes are used to evaluate the accuracy and effectiveness of this segmentation method. The experimental results show that the segmentation optimization method proposed in this paper has high accuracy and data integrity. |
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| Keywords: | indoor adherent point cloud deep learning labeled point cloud classification Euclidean algorithm minimum cut |
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