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.

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  1. Gao H, Geng G (2019) Classification of 3D terracotta warrior fragments based on deep learning and template guidance. IEEE Access 8:4086

    Article  Google Scholar 

  2. Griffiths D, Boehm J (2019) A review on deep learning techniques for 3D sensed data classification. Remote Sens 11(12):1499

    Article  Google Scholar 

  3. Kampel M et al. (2001) Classification of archaeological fragments using profile primitives. In: Proceedings of the 25th workshop of the austrian association for pattern recognition, na

  4. Kampel M, Sablatnig R (2000) Color classification of archaeological fragments. In: Proceedings 15th international conference on pattern recognition. ICPR-2000, IEEE

  5. Kang X et al (2015) Classification of cultural relic fragments based on salient geometric features. J Graphics 36(4):551–556

    Google Scholar 

  6. Karasik A, Smilansky U (2011) Computerized morphological classification of ceramics. J Archaeol Sci 38(10):2644–2657

    Article  Google Scholar 

  7. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980

  8. Li Y et al. (2018) Pointcnn: Convolution on x-transformed points. In: Advances in neural information processing systems

  9. Liu X (2019) Research on residual network identification and multi-feature mosaic technology for cultural relics restoration School of Information Science and Technology Northwest University. Master Thesis

  10. Li-Ying Q, Ke-Gang W (2010) Kernel fuzzy clustering based classification of ancient-ceramic fragments. In: 2010 2nd IEEE international conference on information management and engineering, IEEE

  11. Maturana D, Scherer S (2015) Voxnet: a 3d convolutional neural network for real-time object recognition. In: 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE

  12. Moenning C, Dodgson NA (2003) A new point cloud simplification algorithm. In: Proc. int. conf. on visualization, imaging and image processing

  13. Qi CR et al. (2016) Volumetric and multi-view cnns for object classification on 3d data. In: Proceedings of the IEEE conference on computer vision and pattern recognition

  14. Qi CR et al. (2017) Pointnet: deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition

  15. Qi CR et al. (2017) Pointnet ++: deep hierarchical feature learning on point sets in a metric space. In: Advances in neural information processing systems

  16. Smith P et al. (2010) Classification of archaeological ceramic fragments using texture and color descriptors. In: 2010 IEEE computer society conference on computer vision and pattern recognition-workshops, IEEE

  17. Su H et al. (2015) Multi-view convolutional neural networks for 3d shape recognition. In: Proceedings of the IEEE international conference on computer vision

  18. Szegedy C et al. (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence

  19. Wang, Y. (2019). Research on the classification algorithm of terracotta warrior fragments based on the optimization model of convolutional neural network. School of Information Science and Technology, Northwest University. Master Thesis

  20. Wang Q, Kim M-K (2019) Applications of 3D point cloud data in the construction industry: a fifteen-year review from 2004 to 2018. Adv Eng Inform 39:306–319

    Article  Google Scholar 

  21. Wu Z et al. (2015) 3d shapenets: a deep representation for volumetric shapes. In: Proceedings of the IEEE conference on computer vision and pattern recognition

  22. Xie Y et al. (2019) A review of point cloud semantic segmentation. arXiv preprint arXiv:1908.08854

  23. Yang W et al. (2017) Classification of terra-cotta warriors fragments based on multi-feature and SVM. J Northwest Univ,

  24. Zhao F, Geng G (2018) Fragments classification method of terracotta warriors based on region and shape features. J Geomat Sci Technol 35(6):584–588

    Google Scholar 

  25. Zhou P et al. (2011) Ancient porcelain shards classifications based on color features. In: 2011 Sixth international conference on image and graphics, IEEE

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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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Correspondence to Xin Cao or Kang Li.

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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).

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  • Terracotta warriors fragments
  • Virtual restoration
  • Deep learning
  • Point cloud
  • Texture information