Chu, YiyaoWang, WenchengUmetani, NobuyukiWojtan, ChrisVouga, Etienne2022-10-042022-10-0420221467-8659https://doi.org/10.1111/cgf.14652https://diglib.eg.org:443/handle/10.1111/cgf14652Existing methods for skeleton extraction have limitations in terms of the amount of memory space available, as the model must be allocated to the random access memory. This challenges the treatment of out-of-core models. Although applying out-of-core simplification methods to the model can fit in memory, this would induce distortion of the model surface, and so causing the skeleton to be off-centered or changing the topological structure. In this paper, we propose an efficient out-of-core method for extracting skeletons from large volumetric models. The method takes a volumetric model as input and first computes an out-of-core distance transform. With the distance transform, we generate a medial mesh to capture the prominent features for skeleton extraction, which significantly reduces the data size and facilitates the process of large models. At last, we contract the medial mesh in an out-of-core fashion to generate the skeleton. Experimental results show that our method can efficiently extract high-quality curve skeletons from large volumetric models with small memory usage.CCS Concepts: Computing methodologies → Shape modeling; Shape analysisComputing methodologies → Shape modelingShape analysis10.1111/cgf.146521-1212 pages