Abstract
We research a 3D model retrieval algorithm on the basis of multi-view SIFT features in this paper. By projecting the 3D model from multiple viewpoints, omnidirectional 2D depth images are obtained and from which SIFT features are extracted. Based on k-means clustering algorithm, we establish the codebook according to the proportion of SIFT features number of various shape types and whole SIFT features respectively. As a result, the former is much faster, so it is utilized to build the codebook in our paper. All the SIFT features associated with a model are clustered to generate a simplified vector by a histogram manner. For the similarity matching, Kullback-Leibler divergence is used to calculate distance between simplified vectors. Experiments show the algorithm based on multi-view SIFT feature can gain a satisfactory retrieval result for multiple shape benchmarks.
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References
Yang, Y.B., Lin, H., Zhu, Q.: Content-Based 3D Model Retrieval: A Survey. Chinese Journal of Computers 27, 1297–1310 (2004) (in Chinese)
Daras, P., Axenopoulos, A.: A 3D shape retrieval framework supporting multimodal queries. IJCV 89, 229–247 (2010)
Pu, J.T., Liu, Y., Xin, G.Y.: A 3D Model Retrieval Method Based on the Similarity Between 2D Polygon Sets. Journal of Image and Graphics 9, 1437–1442 (2004) (in Chinese)
Chaouch, M., Verroust-Blondet, A.: A new descriptor for 2D depth image indexing and 3D model retrieval. In: Proc. ICIP 2007, vol. 6, pp. 373–376 (2007)
Napoleon, T., Adamek, T., Schmitt, F., O’Connor, N.E.: SHREC 2008 entry: Multi-view 3D retrieval using multi-scale contour representation. 2008 IEEE International Conference on Shape Modeling and Applications (2008)
Lowe, D.G.: Object recognition from local scale-invariant features. In: 7th IEEE International Conference on Computer Vision, Kerkyra, Greece, pp. 1150–1157 (1999)
Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Wang, R.R., Ma, J.W., Chen, X.: SIFT Algorithm Based on Visual Matching Window for Registration Between Multi-sensor Imagery. Geomatics and Information Science of Wuhan University 36, 163–166 (2011) (in Chinese)
Zeng, L., Zhai, Y.: A SIFT matching algorithm based on pin-hole camera model. In: 2010 3rd International Conference on Computational Intelligence and Industrial Application (PACIIA), pp. 272–276 (2010) (in Chinese)
McGill 3D shape benchmark, http://www.cim.mcgill.ca/shape/benchMark/
Shilane, P., Min, P., Kazhdan, M., Funkhouser, T.: The Princeton Shape Benchmark. In: Proc. SMI 2004, pp. 167–178 (2004), http://shape.cs.princeton.edu/benchmark/
Jayanti, S., Kalyanaraman, Y., Iyer, N., Ramani, K.: Developing an Engineering Shape Benchmark for CAD Models. In: Computer-Aided Design, vol. 38, pp. 939–953 (2006)
Ohbuchi, R., Osada, K., Furuya, T., Banno, T.: Salient Local Visual Features for Shape Based 3D Model Retrieval. In: Proc. IEEE Shape Modeling International (SMI), June 4-6, Brook, Stony (2008)
Osada, R., Funkhouser, T., Chazelle, B., Dobkin, D.: Shape Distributions. ACM Transactions on Graphics 21, 807–832 (2002)
Ohbuchi, R., Nakazawa, M., Takei, T.: Retrieving 3D Shapes Based On Their Appearance. In: 5th ACM SIGMM International Workshop on Multimedia Information Retrieval, Berkeley, Californica, USA, pp. 39–45 (2003)
Ohbuchi, R., Minamitani, T., Takei, T.: Shape-Similarity Search of 3D Models by using Enhanced Shape Functions. International Journal of Computer Applications in Technology (IJCAT) 23, 70–85 (2005)
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Hua, S., Jiang, Q., Zhong, Q. (2011). 3D Model Retrieval Based on Multi-View SIFT Feature. In: Ma, M. (eds) Communication Systems and Information Technology. Lecture Notes in Electrical Engineering, vol 100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21762-3_21
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DOI: https://doi.org/10.1007/978-3-642-21762-3_21
Publisher Name: Springer, Berlin, Heidelberg
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