Abstract
We consider 3D object retrieval in which a polygonal mesh serves as a queryand similar objects are retrieved from a collection of 3D objects. Algorithms proceed first bya normalization step in which models are transformed into canonical coordinates. Second, feature vectors are extracted and compared with those derived from normalized models in the search space. In the feature vector space nearest neighbors are computed and ranked. Retrieved objects are displayed for inspection, selection, and processing. Our feature vectors are based on rays cast from the center of mass of the object. For each raythe object extent in the raydirection yields a sample of a function on the sphere. We compared two kinds of representations of this function, namelyspherical harmonics and moments. Our empirical comparison using precision-recall diagrams for retrieval results in a data base of 3D models showed that the method using spherical harmonics performed better.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
K. Arbter, W.E. Snyder, H. Burkhardt, G. Herzinger, “Application of affineinvariant Fourier descriptors to recognition of 3-D objects”, IEEE Trans. on Pattern Analysis and Machine Intelligence, 12 (1990) 640–647.
M. Ankerst, G. Kastenmüller, H.-P. Kriegel, T. Seidl, “3D shape histograms for similaritysearc h an classification in spatial databases”, Proc. 6th Intern. Symp. on Spatial Databases (SSD’99)., Hong Kong, China, Springer-Verlag, 1999.
N. Canterakis, “3D Zernike moments and Zernike affine invariants for 3D image analysis and recognition”, Proc. 11th Intern. Conf. on Image Analysis., Kanger-lussuaq, Greenland, June 1999.
D.M. Healy, D. Rockmore, P. Kostelec, and S. Moore, “FFTs for the 2-sphere-Improvements and variations,” Advances in Applied Mathematics, (to appear). Preprint and corresponding software, SpharmonicKit, are available at: http://www.cs.dartmouth.edu/~geelong/sphere/.
M. Heczko, D. Keim, D. Saupe, and D.V. Vranić, “A method for similarity search of 3D objects”, Proc. of BTW 2001, Oldenburg, Germany, pp. 384–401, 2001.
MPEG Video Group, “MPEG-7 Visual part of eXperimetation Model (version 9.0),” Doc. ISO/MPEG N3914, Pisa, January, 2001.
M. Novotni and R. Klein, “A geometric approach to 3D object comparison,” Proc. of Shape Modelling International (SMI 2001), Genova, Italy, 2001, (to appear).
E. Paquet, A. Murching, T. Naveen, A. Tabatabai, and M. Rioux, “Description of shape information for 2-D and 3-D objects,” Signal Processing: Image Communication, 16:103–122, 2000.
M.T. Suzuki, T. Kato, and N. Otsu, “A similarityretriev al of 3D polygonal models using rotation invariant shape descriptors,” IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC2000), Nashville, Tennessee, pp. 2946–2952, 2000.
D.V. Vranić and D. Saupe, “3D model retrieval,” Proc. of Spring Conf. on Comp. Graph. and its Appl. (SCCG2000), Budmerice, Slovakia, pp. 89–93, May 2000.
D.V. Vranić and D. Saupe, “Tools for 3D-object retrieval: Karhunen-Loeve Transform and spherical harmonics,” to appear, IEEE Workshop on Multimedia Signal Processing (MMSP’2001), Cannes, France, Oct. 2001.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Saupe, D., Vranić, D.V. (2001). 3D Model Retrieval with Spherical Harmonics and Moments. In: Radig, B., Florczyk, S. (eds) Pattern Recognition. DAGM 2001. Lecture Notes in Computer Science, vol 2191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45404-7_52
Download citation
DOI: https://doi.org/10.1007/3-540-45404-7_52
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42596-0
Online ISBN: 978-3-540-45404-5
eBook Packages: Springer Book Archive