A point cloud simplification method using clustering and saliency for cultural heritage reconstruction

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Publicado en:Heritage Science vol. 13, no. 1 (Dec 2025), p. 445
Autor principal: Li, Jian
Otros Autores: Peng, Chenyang, Gu, Wanfa, Han, Guohe, Zhu, Jin, Tao, Yiwen, Cui, Hao, Jin, Xiaoqian
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Springer Nature B.V.
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Acceso en línea:Citation/Abstract
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024 7 |a 10.1038/s40494-025-02016-y  |2 doi 
035 |a 3249128519 
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100 1 |a Li, Jian  |u Zhengzhou University, School of the Geo-Science & Technology, Zhengzhou, China (GRID:grid.207374.5) (ISNI:0000 0001 2189 3846); Zhengzhou University, Archaeological Innovation Center, Zhengzhou, China (GRID:grid.207374.5) (ISNI:0000 0001 2189 3846) 
245 1 |a A point cloud simplification method using clustering and saliency for cultural heritage reconstruction 
260 |b Springer Nature B.V.  |c Dec 2025 
513 |a Journal Article 
520 3 |a With the rapid development of 3D scanning technologies, high-density point clouds of cultural heritage artifacts such as stone carvings, statues pose significant challenges in storage, processing, and accurate reconstruction. This paper proposes a point cloud simplification method tailored for cultural heritage applications, combining clustering and saliency analysis to preserve intricate surface details critical for archaeological studies. By segmenting point clouds into clusters with normal vector constraints and evaluating saliency through roughness and curvature metrics, our method adaptively retains primary features including engraved patterns weathered textures while simplifying non-feature regions. Experiments on stone carvings from the Northern Song Imperial Mausoleum, Terracotta Warriors, and Stanford datasets demonstrate that the algorithm effectively avoids mesh holes and maintains geometric fidelity, enabling efficient 3D reconstruction for heritage conservation. This work bridges advanced point cloud processing with practical archaeological needs, offering a robust tool for digitizing and analyzing cultural relics with minimal loss of historically significant details. 
653 |a Datasets 
653 |a Deep learning 
653 |a Salience 
653 |a Clustering 
653 |a Engraving 
653 |a Three dimensional models 
653 |a Stone 
653 |a Simplification 
653 |a Historic artifacts 
653 |a Methods 
653 |a Archaeology 
653 |a Algorithms 
653 |a Reconstruction 
653 |a Terracotta 
653 |a Neighborhoods 
653 |a Inscriptions 
653 |a Entropy 
653 |a Cultural resources 
653 |a Cultural heritage 
700 1 |a Peng, Chenyang  |u Zhengzhou University, School of the Geo-Science & Technology, Zhengzhou, China (GRID:grid.207374.5) (ISNI:0000 0001 2189 3846); Henan Thinker Automatic Equipment Co. Ltd, Zhengzhou, China (GRID:grid.207374.5) 
700 1 |a Gu, Wanfa  |u Henan Provincial Institute of Cultural Relics and Archaeology, Zhengzhou, China (GRID:grid.207374.5) 
700 1 |a Han, Guohe  |u Zhengzhou University, Archaeological Innovation Center, Zhengzhou, China (GRID:grid.207374.5) (ISNI:0000 0001 2189 3846); Zhengzhou University, School of Archaeology and Cultural Heritage, Zhengzhou, China (GRID:grid.207374.5) (ISNI:0000 0001 2189 3846) 
700 1 |a Zhu, Jin  |u Zhengzhou University, Archaeological Innovation Center, Zhengzhou, China (GRID:grid.207374.5) (ISNI:0000 0001 2189 3846); Zhengzhou University, School of Archaeology and Cultural Heritage, Zhengzhou, China (GRID:grid.207374.5) (ISNI:0000 0001 2189 3846) 
700 1 |a Tao, Yiwen  |u Zhengzhou University, School of Mathematics and Statistics, Zhengzhou, China (GRID:grid.207374.5) (ISNI:0000 0001 2189 3846) 
700 1 |a Cui, Hao  |u Zhengzhou University, School of the Geo-Science & Technology, Zhengzhou, China (GRID:grid.207374.5) (ISNI:0000 0001 2189 3846); Zhengzhou University, Archaeological Innovation Center, Zhengzhou, China (GRID:grid.207374.5) (ISNI:0000 0001 2189 3846) 
700 1 |a Jin, Xiaoqian  |u Henan Provincial Institute of Cultural Relics and Archaeology, Zhengzhou, China (GRID:grid.207374.5) 
773 0 |t Heritage Science  |g vol. 13, no. 1 (Dec 2025), p. 445 
786 0 |d ProQuest  |t Materials Science Database 
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