Self-adaptive Simplification Algorithm for Suggestive-contours preserving

Abstract

Abstract: It is important that features quickly extract in large-scale model retrieval. The preprocessing of 3D model simplified the mesh, not changed the contours. Based on the data structure of the cross linked list, a framework for self-adaptive algorithm was proposed, according to mesh structure, it automatically selected the rate to simplify. Besides, by computing the related mesh principal curvatures, the edge that some condition was met, were put in the structure of heap for simplification operating. In the experiment, the models were divided into the complex model and the common model. The program could simplify the operation of the mesh. Moreover, the comparative experiment of mesh simplification efficiency was conducted. It could be found that the efficiency of complex model was less than common model. Besides, the different methods were compared, the proposed method was better in time consuming facet. Then, extracted suggestive contours experiment was done. The result show proposed method is better. The best simply rate can been computed by the proposed self-adaptive algorithm.

Key words: model retrieval, cross linked list, self-adaptive, principal curvature, heap, suggestive contours

CLC Number:

TP391.9

Zhou Wen, Jia Jinyuan. Self-adaptive Simplification Algorithm for Suggestive-contours preserving[J]. Journal of System Simulation, 2016, 28(9): 2176-2185.

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