Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W2, 57-61, 2016
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W2/57/2016/
doi:10.5194/isprs-archives-XLII-2-W2-57-2016
 
05 Oct 2016
3D BUILDING MODELS SEGMENTATION BASED ON K-MEANS++ CLUSTER ANALYSIS
C. Zhang and B. Mao College of Information Engineering, Collaborative Innovation Center for Modern Grain Circulation and Safety, Jiangsu Key Laboratory of Modern Logistics, Nanjing University of Finance & Economics, Nanjing, China
Keywords: 3D model segmentation, cluster segmentation, 3D building model, K-means++ cluster Abstract. 3D mesh model segmentation is drawing increasing attentions from digital geometry processing field in recent years. The original 3D mesh model need to be divided into separate meaningful parts or surface patches based on certain standards to support reconstruction, compressing, texture mapping, model retrieval and etc. Therefore, segmentation is a key problem for 3D mesh model segmentation. In this paper, we propose a method to segment Collada (a type of mesh model) 3D building models into meaningful parts using cluster analysis. Common clustering methods segment 3D mesh models by K-means, whose performance heavily depends on randomized initial seed points (i.e., centroid) and different randomized centroid can get quite different results. Therefore, we improved the existing method and used K-means++ clustering algorithm to solve this problem. Our experiments show that K-means++ improves both the speed and the accuracy of K-means, and achieve good and meaningful results.
Conference paper (PDF, 1027 KB)


Citation: Zhang, C. and Mao, B.: 3D BUILDING MODELS SEGMENTATION BASED ON K-MEANS++ CLUSTER ANALYSIS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W2, 57-61, doi:10.5194/isprs-archives-XLII-2-W2-57-2016, 2016.

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