基于曲率特征的文物点云分类降采样与配准方法

朱婧怡; 杨鹏程; 孟杰; 张津京; 崔嘉宝; 代阳

doi:10.37188/CO.2023-0115

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Chinese Optics > 2023 > Accepted Manuscript

ZHU Jing-yi, YANG Peng-cheng, MENG Jie, ZHANG Jin-jing, CUI Jia-bao, DAI Yang. A point cloud classification downsampling and registration method for artifacts based on curvature features[J]. Chinese Optics. doi: 10.37188/CO.2023-0115

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

doi: 10.37188/CO.2023-0115

1.
School of Mechanical and Electrical Engineering, Xi'an Polytechnic University, Xi’an 710048, China
2.
Institute of Archaeology, Chinese Academy of Social Sciences, Beijing 100101, China

Funds: Supported by Basic Research Program of Shaanxi Province - Surface Project (No. 2022JM-219); Special Research Program of Shaanxi Education Department (No. 22JK0404).

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Corresponding author: yangpengcheng@xpu.edu.cn
Available Online: 07 Nov 2023

Abstract

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.
- curvature feature,
- curvature threshold,
- classification downsampling,
- point cloud registration,
- digitization of artifacts

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References(27)

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