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Decomposition of Volumetric Models Based on Distance Transform and K-means Clustering

Abstract— This paper describes a simple decomposition technique for volumetric models. 3D polygonal models are converted to volumetric models, and these models are decomposed by a standard K-means clustering technique. Although the standard K-means clustering technique uses randomly generated initial seeds to determine the starting centroids of the clusters, our technique uses distance maps to determine unique centroids. Preliminary experiments are conducted on a database of 3D models with various shapes. Our method shows better decomposition results compared to random decomposition results, since the technique uses a 3D model’s geometrical features.

Index Terms— K-means, distance transform volumetric data, voxel, decomposition, 3d model.

Motofumi T. Suzuki
The Open University of Japan, JAPAN

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Cite: Motofumi T. Suzuki, "Decomposition of Volumetric Models Based on Distance Transform and K-means Clustering," Proceedings of 2016 6th International Workshop on Computer Science and Engineering, pp. 632-635, Tokyo, 17-19 June, 2016.