基于法向量和面状指数特征的文物点云棱界配准方法 -

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基于法向量和面状指数特征的文物点云棱界配准方法

doi:10.37188/CO.2022-0156

杨鹏程^1,,,
杨朝^1,,
孟杰¹,
肖渊¹,
崔嘉宝^{2, 3}

1.
西安工程大学机电工程学院, 陕西西安 710048
2.
北京大学考古文博学院, 北京100871
3.
山西大学云冈学研究院, 山西太原 030006

基金项目:陕西省自然科学基础研究计划——面上项目（No. 2022JM-219）

详细信息

作者简介:
杨鹏程（1985—），男，副教授，博士，2008年于长沙理工大学获得学士学位，2013年于西安交通大学获得博士学位，主要从事干涉测量、三维数据精确建模、数字图像处理的研究。Email：yangpengcheng@xpu.edu.cn
杨　朝（1996—），男，硕士研究生，2019年于西安工程大学获得学士学位，主要研究方向：全息幻影成像，三维数据精确建模。Email：muyi_meng2021@163.com

中图分类号:TP391.9
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- 被引次数:0
出版历程
- 收稿日期:2022-07-09
- 录用日期:2022-11-03
- 修回日期:2022-09-06
- 网络出版日期:2022-11-22

Aligning method for point cloud prism boundaries of cultural relics based on normal vector and faceted index features

1.
School of Mechanical and Electrical Engineering, Xi'an Polytechnic University, Xi'an 710048, China
2.
School of Archaeology and Museology, Peking University, Beijing 100871, China
3.
Yungangology Institute, Shanxi University, Taiyuan 030006, China

Funds:Supported by Natural Science Basic Research Program of Shaanxi Province (No. 2022JM-219)

More Information

Corresponding author:yangpengcheng@xpu.edu.cn

摘要

摘要:
三维重建是文物信息保护常用的方法，其主要通过点云配准技术重组文物空间的点云信息，配准精度对文物复现有重要影响。针对文物表面复杂点云纹理特征配准存在精度低、鲁棒性差的问题，本文提出一种基于法向量夹角和面状指数特征的局域点云配准方法。首先，根据点云平面特性设定法向量夹角和协方差矩阵阈值，提取同时满足这两个特征的点云特征点；其次，采用K近邻搜索方法提取点云局域特征点集，通过刚性变换使两组点云质心位置重合，完成粗配准；最终，在两点云粗配准的基础上，根据迭代最近点ICP进行精配准。与传统ICP方法进行对比分析，结果显示本文方法的点云配准误差下降了3%，匹配耗时降低了50%，有效地提高了配准精度和效率，增强了点云配准的鲁棒性。
- 点云配准/
- 法向量夹角/
- 协方差矩阵/
- 平面特性/
- 局域特征信息
Abstract:
Three-dimensional reconstruction is a common method for cultural relics information conservation, mainly through point cloud alignment technology to reorganize the spatial point cloud information of cultural relics, and its alignment accuracy has an important impact on cultural relics recovery. To address the problems of low accuracy and poor robustness in the alignment of complex point cloud texture features on the surface of cultural relics, this paper proposes a local point cloud alignment method based on normal vector angle and faceted index features. Firstly, the normal vector angle and covariance matrix thresholds are set according to the point cloud planar characteristics, and the point cloud feature points satisfying both features are extracted; secondly, the point cloud local feature point set is extracted by the K-nearest neighbor search methhod, and the two sets of point cloud center-of-mass positions are overlapped by rigid transformation for coarse alignment; finally, the nearest points are iterated based on ICP for fine alignment. By comparing with the traditional ICP, the point cloud alignment error of the proposed method reduces by 3% and the matching time reduces by 50%, which effectively improves the accuracy and efficiency of alignment and enhances the robustness of point cloud alignment.
- point cloud alignment/
- normal vector pinch angles/
- covariance matrix/
- planar properties/
- local feature information

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图 1不同表面形状的法向量

Figure 1.Normal vectors of different surface shapes

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图 2点云配准方法流程图

Figure 2.Flow chart of point cloud alignment method

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图 3模型实测图

Figure 3.Model measurement diagram

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图 4原始点云图

Figure 4.Primitive point cloud diagrams

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图 5点云法向量特征图

Figure 5.Normal vector feature plots of point cloud

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图 6点云面状指数特征图

Figure 6.Point cloud-like exponential characteristics

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图 7点云特征图

Figure 7.Point cloud feature diagrams

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图 8特征点云局部配准图

Figure 8.Feature point cloud local registration diagram

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图 9兵马俑点云配准对比图

Figure 9.Comparison charts of point cloud registration for terracotta army

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表 1兵马俑点云配准过程实验数据分析

Table 1.Experimental data analysis of terracotta army point cloud registration process

方法	点云数目	特征点数	配准时间/s	RMSE/mm
传统ICP	74320/ 66778	72322/66440	11.73	7.88
本文方法	74320/ 66778	6083/5723	6.22	4.73

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