Abstract The accurate classification of the fragments is a critical step in the restoration of the Terracotta Warriors. However, the traditional manual-based method is time-consuming and labor-intensive, and the accuracy mainly depends on the archeologist’s experience. In this paper, we present a novel classification framework for the 3D Terracotta Warriors fragments. The core of our framework is a dual-modal based neural network, which can incorporate geospatial and texture information of the fragments and output the category of each fragment. The geospatial information is extracted from the point cloud directly. At the same time, a method based on the 3D mesh model and improved Canny edge detection algorithm is proposed to extract the texture information. As to the real-world data experiments, the dataset includes 800 pieces of the arm, 810 pieces of the body, 810 pieces of head and 830 pieces of leg, and the mean accuracy rate is 91.41%, which is better than other existing methods, which only based on geospatial information or texture information. We hope our framework can provide a useful tool for the virtual restoration of the Terracotta Warriors. Graphic abstract

中文翻译：

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基于地理空间和纹理信息的3D兵马俑碎片分类

摘要 残片的准确分类是兵马俑修复的关键步骤。但是，传统的人工方法费时费力，准确性主要取决于考古学家的经验。在本文中，我们为 3D 兵马俑碎片提出了一种新颖的分类框架。我们框架的核心是一个基于双模态的神经网络，它可以结合片段的地理空间和纹理信息并输出每个片段的类别。地理空间信息直接从点云中提取。同时，提出了一种基于3D网格模型和改进的Canny边缘检测算法的纹理信息提取方法。对于真实世界的数据实验，数据集包括 800 个手臂，身体810块，头部810块，腿830块，平均准确率为91.41%，优于其他仅基于地理空间信息或纹理信息的现有方法。我们希望我们的框架能为兵马俑的虚拟修复提供一个有用的工具。图形摘要