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

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.

Automated summary

A novel classification framework for 3D Terracotta Warriors fragments is proposed in this paper, achieving an average accuracy rate of 91.41% by integrating geospatial and texture information with a dual-modal neural network, outperforming existing methods.