Classification of 3D Terracotta Warrior Fragments Based on Deep Learning and Template Guidance
Classification of 3D Terracotta Warrior Fragments Based on Deep Learning and Template Guidance
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DOI：
10.1109/access.2019.2962791
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Hongjuan Gao;Guohua Geng
中科院分区：
文献类型：
--
作者：
Hongjuan Gao;Guohua Geng
兵马俑是2,000多年前为中国第一位皇帝建造的兵马俑。它们是中国最珍贵的出土文物之一。然而，这些文物主要是在碎片中发现的。碎片分类目前是手工进行大量的碎片，这是一个耗时，不准确，主观的任务，考古学家和保护。在这项研究中，提出了一种基于深度学习网络结合模板指导的自动方法来分类兵马俑的3D碎片。这些片段最初使用PointNet进行分类。然后，错误分类的碎片根据其与完整兵马俑模型的最佳匹配进行第二次分类。大量的实验验证了该方法的有效性。结果表明，该方法是迄今为止对3D兵马俑碎片进行分类的最准确的技术。此外，所提出的方法可以显着提高未来的碎片重新组装兵马俑的效率。
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.