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.
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This work was supported in part by the National Natural Science Foundation of China under Grant 61701403, the Project funded by China Post-doctoral Science Foundation under Grant No. 2018M643719, the Young Talent Support Program of the Shaanxi Association for Science and Technology under Grant No.20190107, the National Key Research and Development Program of China under Grant 2017YFB1402103, the Scientific Research Program Funded by Shaanxi Provincial Education Department under Grant No. 18JK0767, and the Natural Science Research Plan Program in Shaanxi Province of China 2017JQ6006.
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Yang, K., Cao, X., Geng, G. et al. Classification of 3D terracotta warriors fragments based on geospatial and texture information. J Vis (2021). https://doi.org/10.1007/s12650-020-00710-6
- Terracotta warriors fragments
- Virtual restoration
- Deep learning
- Point cloud
- Texture information