Abstract
Feature points are particularly simple elements which demonstrate a model efficiently and availably; nonetheless, the points on 3D models cannot be extracted completely yet. Therefore, we propose a new algorithm based on data gravitation to extract the feature points on 3D meshes. First, we select the point with the maximum Gaussian curvature as the initial feature point set. Then, we use farthest point sampling to calculate the farthest distance from feature point set, and add this point into feature point set. Next we use the farthest distance to calculate data gravitation and select the point with the largest data gravitation until the farthest distance is smaller than a given threshold. Finally we get the feature points set on 3D meshes. In our experiments, we compare our algorithm with other algorithms. Results show that our algorithm can capture feature points effectively; consequently, the set of feature points reflects the features of 3D meshes precisely. Moreover, our algorithm is simple and is therefore easy to implement.
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Acknowledgement
This research was supported by National Natural Science Foundation of China under contract (Nos. 61373054, 61203105, 61472164), Natural Science Foundation of Shandong Province under contract (Nos. ZR2012FQ016, ZR2012FM010).
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Wang, C., Kang, D., Zhao, X., Peng, L., Zhang, C. (2016). Extraction of Feature Points on 3D Meshes Through Data Gravitation. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_54
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DOI: https://doi.org/10.1007/978-3-319-42294-7_54
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