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Classification of 3D terracotta warriors fragments based on geospatial and texture information

机译：基于地理空间和纹理信息的3D兵马俑分类

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摘要

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.
机译：碎片的准确分类是恢复兵马俑的关键步骤。然而，传统的基于手动的方法是耗时和劳动密集型，准确性主要取决于考生的经验。在本文中，我们为3D兵马俑碎片提出了一种新的分类框架。我们的框架的核心是基于双模的神经网络，可以包含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网状模型的方法和改进的Canny边缘检测算法来提取纹理信息。对于真实世界的数据实验，数据集包括800件臂，810件的主体，810件头部和830件腿，平均精度率为91.41％，这比其他现有方法更好，仅基于地理空间信息或纹理信息。我们希望我们的框架可以为兵马俑的虚拟恢复提供一个有用的工具。

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