Abstract
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.
Highlights
Multimedia has gone through three waves so far: sound, image and video
With the development of 3D scanning and relevant technologies, 3D digital geometry models have become a new type of multimedia (Sun, 2005a), which have been intensively used in many fields such as industrial manufacturing, entertainment, biological medicine, architectural design, visualization in scientific computing and etc
Segmentation of 3D mesh model is usually based on certain standards of division, so that the original 3D model can be divided into a set of meaningful simple shape
Summary
Multimedia has gone through three waves so far: sound, image and video. In recent years, with the development of 3D scanning and relevant technologies, 3D digital geometry models have become a new type of multimedia (Sun, 2005a), which have been intensively used in many fields such as industrial manufacturing, entertainment, biological medicine, architectural design, visualization in scientific computing and etc. It is a difficult to automatically segment the 3D buildings in mesh model. It will have significant effect that if we are able to segment, simplify, generalize and rebuild 3D building models to map or urban planning. We propose a method to segment Collada 3D building models into different parts using cluster analysis. Common clustering methods segment 3D mesh models by K-means, as illustrated in Fig., whose efficiency heavily depends on randomized initial seed points (i.e., centroid) and different randomized centroid can get quite different results. Most of 3D mesh segmentation algorithms are inspired by 2D image segmentation, and are extended to 3D mesh.
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