Classification of 3D Terracotta Warrior Fragments Based on Deep Learning and Template Guidance

Abstract

The Terracotta Warriors are terracotta sculptures created for China's first emperor more than 2,000 years ago. They are among the most precious unearthed cultural relics of China. However, these relics have been predominantly found in fragments. Fragment classification is currently performed manually on enormous quantities of fragments, which is a time-consuming, inaccurate, and subjective task for archaeologists and conservators. In this study, an automatic method based on a deep learning network combined with template guidance is proposed to classify 3D fragments of the Terracotta Warriors. The fragments are initially classified using PointNet. Then, misclassified fragments are secondly categorized based on their best match to a complete Terracotta Warrior model. Extensive experiments were performed to verify the effectiveness of the proposed method. The promising results demonstrate that the method is the most accurate technique for classifying 3D Terracotta Warrior fragments to date. Moreover, the proposed method can significantly increase the efficiency of future fragment reassembly for the Terracotta Warriors.