| 基于分裂合并的多模型拟合方法在点云分割中的应用 |
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| 引用本文： | 张良培,张云,陈震中,肖佩珮,罗斌.基于分裂合并的多模型拟合方法在点云分割中的应用[J].测绘学报,2018,47(6):833-843. |
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| 作者姓名： | 张良培 张云 陈震中 肖佩珮 罗斌 |
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| 作者单位： | 武汉大学测绘遥感信息工程国家重点实验室, 湖北 武汉 430079 |
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| 基金项目： | 国家自然科学基金（61261130587；61571332） |
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| 摘 要： | 本文基于机器视觉探讨数字摄影测量三维构像下的智能数据处理要素之二：海量点云分割处理技术。多模型拟合方法通过将点云拟合到不同模型中，依照点云空间分布特征和几何结构特征进行分割。针对点云数据量巨大、分布不均匀、结构复杂等特性，本文提出一种基于多模型拟合的点云分割方法。首先通过降采样，采用基于密度分布的聚类方法，实现对点云的预分割。在预分割基础上，利用基于分裂合并的多模型拟合方法对点云进行后续拟合分割。针对平面和弧面，本文采用不同的拟合方式，最终实现对室内密集点云分割。试验结果表明，该方法能够在无须提前设置模型数目的情况下实现点云的自动分割。且相较于现有的点云分割技术，此方法相较于现今的常规方法能取得更好的分割效果，在分割的正确率上要高于现有的常规分割方法，在处理相同数据量的点云分割时，能够达到远低于常规方法的时间消耗。通过本文提出的三维点云分割方法能够实现将大规模、复杂三维点云数据分割为较为精细、具有准确模型参数的三维几何图元，为后续实现大规模、复杂场景的精确三维构象提供有力支持。
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| 关 键 词： | 机器视觉 三维构像 点云分割 分裂合并 多模型拟合 |
| 收稿时间： | 2018-01-06 |
| 修稿时间： | 2018-03-29 |
Splitting and Merging Based Multi-model Fitting for Point Cloud Segmentation |
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| ZHANG Liangpei,ZHANG Yun,CHEN Zhenzhong,XIAO Peipei,LUO Bin.Splitting and Merging Based Multi-model Fitting for Point Cloud Segmentation[J].Acta Geodaetica et Cartographica Sinica,2018,47(6):833-843. |
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| Authors: | ZHANG Liangpei ZHANG Yun CHEN Zhenzhong XIAO Peipei LUO Bin |
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| Institution: | The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China |
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| Abstract: | This paper deals with the massive point cloud segmentation processing technology on the basis of machine vision,which is the second essential factor for the intelligent data processing of three dimensional conformation in digital photogrammetry.In this paper,multi-model fitting method is used to segment the point cloud according to the spatial distribution and spatial geometric structure of point clouds by fitting the point cloud into different geometric primitives models.Because point cloud usually possesses large amount of 3D points,which are uneven distributed over various complex structures,this paper proposes a point cloud segmentation method based on multi-model fitting.Firstly,the pre-segmentation of point cloud is conducted by using the clustering method based on density distribution.And then the follow fitting and segmentation are carried out by using the multi-model fitting method based on split and merging.For the plane and the arc surface,this paper uses different fitting methods,and finally realizing the indoor dense point cloud segmentation.The experimental results show that this method can achieve the automatic segmentation of the point cloud without setting the number of models in advance.Compared with the existing point cloud segmentation methods,this method has obvious advantages in segmentation effect and time cost,and can achieve higher segmentation accuracy.After processed by method proposed in this paper,the point cloud even with large-scale and complex structures can often be segmented into 3D geometric elements with finer and accurate model parameters,which can give rise to an accurate 3D conformation. |
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