Abstract
In this paper we propose a feature extraction method for shape-based retrieval of 3D models, which uses the mutual intersected meshes between model and growing spheres. Since the feature descriptor of 3D model should be invariant to translation, rotation and scaling, we firstly normalize the pose of 3D models using principal component analysis method. We therefore represent them in a canonical coordinate system. The proposed algorithm for feature extraction is as follows. We generate a unit-size circum-sphere around 3D model, and locate the model in the center of the circum-sphere. We produce the concentric spheres with a different radius (r i =i/n, i=1,2,...,n). After finding the intersected meshes between the concentric spheres and object, we compute the mean curvatures of the meshes for each growing spheres, and use them as the feature descriptor of 3D model. Experimental evidence shows that our algorithm outperforms other methods for 3D indexing and retrieval. To index the multi-dimensional feature vectors, we use R*-tree structure.
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Lee, K.H., Kim, N.W., Choi, J.S. (2005). Efficient 3D Model Retrieval Method Using Geometric Characteristics in Intersected Meshes. In: Torra, V., Narukawa, Y., Miyamoto, S. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2005. Lecture Notes in Computer Science(), vol 3558. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11526018_37
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DOI: https://doi.org/10.1007/11526018_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27871-9
Online ISBN: 978-3-540-31883-5
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