A point cloud classification downsampling and registration method for artifacts based on curvature features
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摘要:
三维重构是文物数字化的关键技术，其中三维点云配准精度是评估重构质量优劣的重要指标之一。实际采样中，文物点云细节信息繁多，传统降采样后易出现细节缺失从而影响配准精度。为了解决这一问题，本文提出了一种基于曲率特征的文物点云分类降采样与配准方法。首先，通过线性矩阵 测量获取文物的三维点云数据。其次，计算所有点的曲率值，并设置曲率阈值进行点云分类，不同点集按照其特征属性进行不同权重的降采样，从而最大限度地保留点云的形态特征和细节信息。最后通过求解刚性变换模型实现点云的配准。点云配准前的降采样处理后点云数据降至原始点云的1/3,与传统的整体降采样ICP方法对比，平均距离从0.89 mm约降至0.59 mm,标准偏差从0.29 mm约降至0.18 mm。在降低点云数据的同时也保证了配准的精度，适用于不同类型的文物点云数据。
Abstract:3D reconstruction is crucial for digitizing artifacts, and the accuracy of 3D point cloud registration is a significant metric for evaluating the reconstruction quality. In practice, artifact point cloud data includes numerous details, and using conventional downsampling methods may result in the loss of such details, thereby affecting registration accuracy. This paper proposes a method for downsampling and registering artifacts point clouds based on curvature features. First, 3D point clouds data of artifacts are obtained using linear matrix laser measurement. Next, the curvature values of all points are calculated, and a curvature threshold is set for point cloud classification. We downsample different point sets based on their feature attributes, with varying weights assigned to retain the shape features and details of the point cloud as much as possible. Finally, point cloud registration is achieved through the use of a rigid transformation model. Compared to the traditional global downsampling ICP method, the downsampling processing before point cloud registration reduces the point cloud data to 1/3 of the original size. The average distance decreases from approximately 0.89 mm to 0.59 mm, while the standard deviation decreases from about 0.29 mm to 0.18 mm. This approach guarantees the accuracy of downsampling and registration and is applicable to various artifacts point cloud data.
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图 1点云分类与降采样方法流程图
Figure 1.Flowchart of point cloud classification and downsampling methods
表 1本文分类降采样数据
Table 1.The classification downsampling data of this paper
原始点云数量 配准后点云数量 平均距离/mm 标准偏差/mm 1950581 422423 152816 575239 
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表 2仿真铜像点云配准实验过程数据分析
Table 2.Experiment process data analysis of point cloud registration for simulated copper statue
方法 原始点云数量 配准后点云数量 平均距离/mm 标准偏差/mm 传统整体
降采样后3471705 985621 0.891086 0.296167 本文分类
降采样后3471705 981584 0.591977 0.180786 下载:
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