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Auto clustering of 3D details using shape skeleton


In the era of digital twins, high-definition 3D point clouds of cultural relics, such as the bronze drums of ancient Southeast Asia and China, are increasingly available as digital heritage. This study applies an automatic hierarchical clustering method to compare and cluster 14 unstructured 3D models of frogs on drums based on the dissimilarity metric of the minimum error from 2,000 iterations of global registration. Furthermore, this study compares two forms of 3D presentation: surface points and 3D shape skeletons. The experimental results on 14 high-definition frogs showed that four groups – three-legged with baby, four-legged with baby, three-legged without baby, and four-legged without baby – were consistently (TPR = 0.857) detected, regardless of the 3D presentation using point clouds or shape skeletons. Both basic surface points and advanced shape skeleton effectively clustered 3D heritage details for heritage digital twins and advanced heritage documentation. The findings also imply that geospatial analytics using either surface 3D point clouds or skeleton can shed light on unsupervised learning and quantitative understanding of unstructured point clouds of numerous cultural heritages.

How to cite

Xue, F., Zhang, W., Xu, G., Zhou, Q., and Wu, Y. (2023). Surface or skeleton? Automatic hierarchical clustering of 3D point clouds of bronze frog drums for heritage digital twins. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-M-1-2023, 293–299,


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