基于曲率特征的文物点云分类降采样与配准方法

A point cloud classification downsampling and registration method for cultural relics based on curvature features

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,通过线性矩阵激光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方法相比,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。

3D reconstruction is crucial for digitization of cultural relics,and the accuracy of 3D point cloud registration is a significant metric for evaluating the reconstruction quality.In practice,cultural relics point cloud data includes numerous details,and using conventional downsampling methods may result in the loss of such details,thereby affecting registration accuracy.We propose a point cloud classification downsampling and registering method for cultural relics based on curvature features.First,3D point clouds data of cultural relics 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.Different point sets are carried out downsampling with different weights according to their feature attributes to retain the shape features and details of the point cloud as much as possible.Finally,point cloud registration is achieved through calculating the rigid transformation model.Compared to the traditional global downsampling ICP method,the point cloud data of the downsampling processing before point cloud registration reduces 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 cultural relics point cloud data.