Aligning method for point cloud prism boundaries of cultural relics based on normal vector and faceted index features
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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。
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.
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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
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/
6677872322/66440 11.73 7.88 本文方法 6083/5723 6.22 4.73 
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