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Erschienen in: Journal of Visualization 2/2021

03.01.2021 | Regular Paper

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

verfasst von: Kang Yang, Xin Cao, Guohua Geng, Kang Li, Mingquan Zhou

Erschienen in: Journal of Visualization | Ausgabe 2/2021

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Abstract

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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Metadaten
Titel
Classification of 3D terracotta warriors fragments based on geospatial and texture information
verfasst von
Kang Yang
Xin Cao
Guohua Geng
Kang Li
Mingquan Zhou
Publikationsdatum
03.01.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal of Visualization / Ausgabe 2/2021
Print ISSN: 1343-8875
Elektronische ISSN: 1875-8975
DOI
https://doi.org/10.1007/s12650-020-00710-6

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