The emergence of the three-dimensional (3D) scanner has greatly benefited archeology, which can now store cultural heritage artifacts in computers and present them on the Internet. As many Terracotta Warriors have been predominantly found in fragments, the pre-processing of these fragments is very important. The raw point cloud of the fragments has lots of redundant points; it requires an excessively large storage space and much time for post-processing. Thus, an effective method for point cloud simplification is proposed for 3D Terracotta Warrior fragments. First, an algorithm for extracting feature points is proposed that is based on local structure. By constructing a k-dimension tree to establish the k-nearest neighborhood of the point cloud, and comparing the feature discriminant parameter and characteristic threshold, the feature points, as well as the non-feature points, are separated. Second, a deep neural network is constructed to simplify the non-feature points. Finally, the feature points and the simplified non-feature points are merged to form the complete simplified point cloud. Experiments with the public point cloud data and the real-world Terracotta Warrior fragments data are designed and conducted. Excellent simplification results were obtained, indicating that the geometric feature can be preserved very well.

中文翻译：

---

基于局部结构和深度神经网络的3D兵马俑碎片简化方法

三维（3D）扫描仪的出现使考古学大大受益，考古学现在可以将文化遗产文物存储在计算机中，并将其显示在Internet上。由于主要在碎片中发现许多兵马俑，因此这些碎片的预处理非常重要。片段的原始点云具有很多冗余点；它需要过大的存储空间和大量时间进行后处理。因此，针对3D兵马俑碎片提出了一种有效的点云简化方法。首先，提出了一种基于局部结构的特征点提取算法。通过构造一个k维树来建立点云的k近邻，并比较特征判别参数和特征阈值，特征点，与非功能点分开。其次，构建了深度神经网络以简化非特征点。最后，将特征点和简化的非特征点合并以形成完整的简化点云。设计并进行了针对公共点云数据和现实世界中的兵马俑碎片数据的实验。获得了极好的简化结果，表明可以很好地保留几何特征。设计并进行了针对公共点云数据和现实世界中的兵马俑碎片数据的实验。获得了极好的简化结果，表明可以很好地保留几何特征。设计并进行了针对公共点云数据和现实世界中的兵马俑碎片数据的实验。获得了出色的简化结果，表明可以很好地保留几何特征。