Abstract
The view-based 3D model retrieval systems represent a 3D model using its projected views, and retrieve 3D models by comparing the projected views. Most of the existing view-based 3D model retrieval systems only analyze the features of the projected views, while the spatial arrangements of the viewpoints are not well considered. In this paper, we propose a new 3D model descriptor called sphere image, which is defined as a sphere with a large number of viewpoints distributed on it. Each viewpoint is regarded as a ”pixel”, associated with a projected view. The feature of the projected view is quantized into a vector, regarded as the ”intensity”. We also propose a probabilistic graphical model for 3D model matching, and develop a 3D model retrieval system to test our approach. The proposed approach was evaluated on the Princeton shape benchmark. Experimental results indicate that our approach outperforms most of the existing 3D model retrieval systems in respect of retrieval precision and computation cost.
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References
Tangelder, J., Veltkamp, R.: A survey of content based 3d shape retrieval methods. In: Proceedings of the Shape Modeling International (SMI), pp. 145–156 (2004)
Bustos, B., Keim, D.A., Saupe, D., Schreck, T., Vranić, D.V.: Feature-based similarity search in 3d object databases. ACM Computing Surveys 37, 345–387 (2005)
Bimbo, A.D., Pala, P.: Content-based retrieval of 3d models. ACM Trans. on Multimedia Computing, Communications and Applications 2, 20–43 (2006)
Kazhdan, M., Funkhouser, T., Rusinkiewicz, S.: Rotation invariant spherical harmonic representation of 3d shape descriptors. In: Proceedings of the Eurographics/ACM SIGGRAPH Symposium on Geometry Processing, pp. 156–164. Eurographics Association (2003)
Ohbuchi, R., Osada, K., Furuya, T., Banno, T.: Salient local visual features for shape-based 3d model retrieval. In: IEEE Int. Conf. on Shape Modeling and Applications, pp. 93–102 (2008)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the 7th IEEE Int. Conf. on Computer Vision, vol. 2, pp. 1150–1157 (1999)
Chen, D., Tian, X., Shen, Y., Ouhyoung, M.: On visual similarity based on 3d model retrieval. Computer Graphics Forum, 223–232 (2003)
Vranić, D., Saupe, D., Richter, J.: Tools for 3d-object retrieval: Karhunen-loeve transform and spherical harmonics. In: IEEE 4th Workshop on Multimedia Signal Processing, pp. 293–298 (2001)
Mademlis, A., Daras, P., Axenopoulos, A., Tzovaras, D., Strintzis, M.G.: Combining topological and geometrical features for global and partial 3-d shape retrieval. IEEE Trans. on Multimedia 10, 819–831 (2008)
Shilane, P., Min, P., Kazhdan, M., Funkhouser, T.: The princeton shape benchmark. In: Proceedings of the Shape Modeling Applications, pp. 167–178 (2004)
Ansary, T.F., Daoudi, M., Vandeborre, J.P.: A bayesian 3d search engine using adaptive views clustering. IEEE Trans. on Multimedia 9, 78–88 (2007)
Ohbuchi, R., Furuya, T.: Accelerating bag-of-features sift algorithm for 3d model retrieval. In: SAMTWorkshop Semantic 3-D Media, pp. 22–30 (2008)
Gao, Y., Wang, M., Zha, Z.J., Tian, Q., Dai, Q., Zhang, N.: Less is more: Efficient 3d object retrieval with query view selection. IEEE Trans. on Multimedia 13, 1007–1018 (2011)
Gao, Y., Wang, M., Tao, D., Ji, R., Dai, Q.: 3-d object retrieval and recognition with hypergraph analysis. IEEE Transactions on Image Processing 21, 4290–4303 (2012)
Gao, Y., Dai, Q., Zhang, N.Y.: 3d model comparison using spatial structure circular descriptor. Pattern Recognition 43, 1142–1151 (2010)
Boiman, O., Irani, M.: Detecting irregularities in images and in video. In: IEEE International Conference on Computer Vision (ICCV), vol. 1, pp. 462–469 (2005)
Canny, J.: A computational approach to edge detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 8, 679–698 (1986)
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient matching of pictorial structures. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 66–73 (2000)
Hamerly, G., Elkan, C.: Learning the k in k-means. In: Neural Information Processing Systems, pp. 281–288. MIT Press (2003)
Osada, R., Funkhouser, T., Chazelle, B., Dobkin, D.: Matching 3d models with shape distributions (2001)
Ohbuchi, R., Otagiri, T., Ibato, M., Takei, T.: Shape-similarity search of three-dimensional models using parameterized statistics. In: Proceedings of the Pacific Graphics, pp. 265–274 (2002)
Wahl, E., Hillenbrand, U., Hirzinger, G.: Surflet-pair-relation histograms: A statistical 3d-shape representation for rapid classification. In: 3DIM 2003, pp. 474–482 (2003)
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Ding, K., Liu, Y. (2013). A Probabilistic 3D Model Retrieval System Using Sphere Image. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37331-2_41
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DOI: https://doi.org/10.1007/978-3-642-37331-2_41
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