Abstract
The retrieval of non-rigid 3D shapes is an important task. A common technique is to simplify this problem to a rigid shape retrieval task by producing a bending-invariant canonical form for each shape in the dataset to be searched. It is common for these techniques to attempt to “unbend” a shape by applying multidimensional scaling (MDS) to the distances between points on the mesh, but this leads to unwanted local shape distortions. We instead perform the unbending on the skeleton of the mesh, and use this to drive the deformation of the mesh itself. This leads to computational speed-up, and reduced distortion of local shape detail. We compare our method against other canonical forms: our experiments show that our method achieves state-of-the-art retrieval accuracy in a recent canonical forms benchmark, and only a small drop in retrieval accuracy over the state-of-the-art in a second recent benchmark, while being significantly faster.
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Li, B.; Godil, A.; Aono, M.; Bai, X.; Furuya, T.; Li, L.; Ló pez-Sastre, R.; Johan, H.; Ohbuchi, R.; Redondo-Cabrera, C.; Tatsuma, A.; Yanagimachi, T.; Zhang, S. SHREC’ 12 track: Generic 3D shape retrieval. In: Proceedings of the 5th Eurographics Conference on 3D Object Retrieval, 119–126, 2012.
Elad, A.; Kimmel, R. On bending invariant signatures for surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 25, No. 10, 1285–1295, 2003.
Pickup, D.; Sun, X.; Rosin, P. L.; Martin, R. R.; Cheng, Z.; Nie, S.; Jin, L. Canonical forms for nonrigid 3D shape retrieval. In: Proceedings of the 2015 Eurographics Workshop on 3D Object Retrieval, 99–106, 2015.
Chen, D.-Y.; Tian, X.-P.; Shen, Y.-T.; Ouhyoung, M. On visual similarity based 3D model retrieval. Computer Graphics Forum Vol. 22, No. 3, 223–232, 2003.
Johnson, A. E.; Hebert, M. Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 21, No. 5, 433–449, 1999.
Tangelder, J. W. H.; Veltkamp, R. C. A survey of content based 3D shape retrieval methods. Multimedia Tools and Applications Vol. 39, No. 3, 441–471, 2008.
Boyer, E.; Bronstein, A. M.; Bronstein, M. M.; Bustos, B.; Darom, T.; Horaud, R.; Hotz, I.; Keller, Y.; Keustermans, J.; Kovnatsky, A.; Litmany, R.; Reininghaus, J.; Sipiran, I.; Smeets, D.; Suetens, P.; Vandermeulen, D.; Zaharescu, A.; Zobel, V. SHREC 2011: Robust feature detection and description benchmark. In: Proceedings of the 4th Eurographics Conference on 3D Object Retrieval, 71–78, 2011.
Smeets, D.; Keustermans, J.; Vandermeulen, D.; Suetens, P. meshSIFT: Local surface features for 3D face recognition under expression variations and partial data. Computer Vision and Image Understanding Vol. 117, No. 2, 158–169, 2013.
Ben-Chen, M.; Gotsman, C. Characterizing shape using conformal factors. In: Proceedings of the 1st Eurographics Conference on 3D Object Retrieval, 1–8, 2008.
Giachetti, A.; Lovato, C. Radial symmetry detection and shape characterization with the multiscale area projection transform. Computer Graphics Forum Vol. 31, No. 5, 1669–1678, 2012.
Sun, J.; Ovsjanikov, M.; Guibas, L. A concise and provably informative multi-scale signature based on heat diffusion. Computer Graphics Forum Vol. 28, No. 5, 1383–1392, 2009.
Lian, Z.; Zhang, J.; Choi, S.; ElNaghy, H.; El-Sana, J.; Furuya, T.; Giachetti, A.; Guler, R. A.; Lai, L.; Li, C.; Li, H.; Limberger, F. A.; Martin, R.; Nakanishi, R. U.; Neto, A. P.; Nonato, L. G.; Ohbuchi, R.; Pevzner, K.; Pickup, D.; Rosin, P.; Sharf, A.; Sun, L.; Sun, X.; Tari, S.; Unal, G.; Wilson, R. C. Non-rigid 3D shape retrieval. In: Proceedings of the 2015 Eurographics Workshop on 3D Object Retrieval, 107–120, 2015.
Pickup, D.; Sun, X.; Rosin, P. L.; Martin, R. R.; Cheng, Z.; Lian, Z.; Aono, M.; Hamza, A. B.; Bronstein, A.; Bronstein, M.; Bu, S.; Castellani, U.; Cheng, S.; Garro, V.; Giachetti, A.; Godil, A.; Han, J.; Johan, H.; Lai, L.; Li, B.; Li, C.; Li, H.; Litman, R.; Liu, X.; Liu, Z.; Lu, Y.; Tatsuma, A.; Ye, J. Shape retrieval of non-rigid 3D human models. In: Proceedings of the 7th Eurographics Workshop on 3D Object Retrieval, 101–110, 2014.
Hilaga, M.; Shinagawa, Y.; Kohmura, T.; Kunii, T. L. Topology matching for fully automatic similarity estimation of 3D shapes. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, 203–212, 2001.
Sfikas, K.; Theoharis, T.; Pratikakis, I. Non-rigid 3D object retrieval using topological information guided by conformal factors. The Visual Computer Vol. 28, No. 9, 943–955, 2012.
Reuter, M.; Wolter, F.-E.; Peinecke, N. Laplace–Beltrami spectra as ‘Shape-DNA’ of surfaces and solids. Computer-Aided Design Vol. 38, No. 4, 342–366, 2006.
Smeets, D.; Hermans, J.; Vandermeulen, D.; Suetens, P. Isometric deformation invariant 3D shape recognition. Pattern Recognition Vol. 45, No. 7, 2817–2831, 2012.
Shamai, G.; Zibulevsky, M.; Kimmel, R. Accelerating the computation of canonical forms for 3D nonrigid objects using multidimensional scaling. In: Proceedings of the 2015 Eurographics Workshop on 3D Object Retrieval, 71–78, 2015.
Lian, Z.; Godil, A.; Xiao, J. Feature-preserved 3D canonical form. International Journal of Computer Vision Vol. 102, No. 1, 221–238, 2013.
Wang, X.-L.; Zha, H. Contour canonical form: An efficient intrinsic embedding approach to matching non-rigid 3D objects. In: Proceedings of the 2nd ACM International Conference on Multimedia Retrieval, Article No. 31, 2012.
Pickup, D.; Sun, X.; Rosin, P. L.; Martin, R. R. Euclidean-distance-based canonical forms for non-rigid 3D shape retrieval. Pattern Recognition Vol. 48, No. 8, 2500–2512, 2015.
Boscaini, D.; Girdziusas, R.; Bronstein, M. M. Coulomb shapes: Using electrostatic forces for deformation-invariant shape representation. In: Proceedings of the 7th Eurographics Workshop on 3D Object Retrieval, 9–15, 2014.
Crane, K.; Weischedel, C.; Wardetzky, M. Geodesics in heat: A new approach to computing distance based on heat flow. ACM Transactions on Graphics Vol. 32, No. 5, Article No. 152, 2013.
Ying, X.; Xin, S.-Q.; He, Y. Parallel chen-han (PCH) algorithm for discrete geodesics. ACM Transactions on Graphics Vol. 33, No. 1, Article No. 9, 2014.
Lian, Z.; Godil, A.; Bustos, B.; Daoudi, M.; Hermans, J.; Kawamura, S.; Kurita, Y.; Lavoué, G.; Nguyen, H. V.; Ohbuchi, R.; Ohkita, Y.; Ohishi, Y.; Porikli, F.; Reuter, M.; Sipiran, I.; Smeets, D.; Suetens, P.; Tabia, H.; Vandermeulen, D. SHREC’ 11 track: Shape retrieval on non-rigid 3D watertight meshes. In: Proceedings of the 4th Eurographics Conference on 3D Object Retrieval, 79–88, 2011.
Lian, Z.; Godil, A.; Sun, X.; Xiao, J. CM-BOF: Visual similarity-based 3D shape retrieval using clock matching and bag-of-features. Machine Vision and Applications Vol. 24, No. 8, 1685–1704, 2013.
Kimmel, R.; Sethian, J. A. Computing geodesic paths on manifolds. Proceedings of the National Academy of Sciences of the United States of the America Vol. 95, No. 15, 8431–8435, 1998.
Borg, I.; Groenen, P. J. F. Modern Multidimensional Scaling: Theory and Applications. Springer-Verlag New York, 2005.
Au, O. K.-C.; Tai, C.-L.; Chu, H.-K.; Cohen-Or, D.; Lee, T.-Y. Skeleton extraction by mesh contraction. In: Proceedings of ACM SIGGRAPH 2008 Papers, Article No. 44, 2008.
Yan, H.-B.; Hu, S.-M.; Martin, R.; Yang, Y.-L. Shape deformation using a skeleton to drive simplex transformations. IEEE Transactions on Visualization and Computer Graphics Vol. 14, No. 3, 693–706, 2008.
Baeza-Yates, R. A.; Ribeiro-Neto, B. A. Modern Information Retrieval: The Concepts and Technology behind Search, 2nd edn. Harlow, England: Pearson Education Ltd., 2011.
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David Pickup obtained his M.Eng. degree from the University of Bristol and Ph.D. degree from the University of Bath. He is currently a postdoctoral research associate in the Visual Computing research group, Cardiff University. His current research centres on three-dimensional shape retrieval.
Xianfang Sun received his Ph.D. from the Institute of Automation, Chinese Academy of Sciences, in 1994. He is currently a senior lecturer in the School of Computer Science & Informatics, Cardiff University, Wales, UK. His main research interests include computer vision, computer graphics, pattern recognition, and artificial intelligence.
Paul L. Rosin is a professor in the School of Computer Science & Informatics, Cardiff University. Previous posts include lecturer in the Department of Information Systems and Computing at Brunel University London, UK, research scientist in the Institute for Remote Sensing Applications at Joint Research Centre, Ispra, Italy, and lecturer at Curtin University of Technology, Perth, Australia. His research interests include the representation, segmentation, and grouping of curves, knowledge-based vision systems, early image representations, low level image processing, machine vision approaches to remote sensing, methods for evaluation of approximation algorithms, etc., medical and biological image analysis, mesh processing, non-photorealistic rendering, and the analysis of shape in art and architecture.
Ralph R. Martin obtained his Ph.D. degree in 1983 from Cambridge University. Since then he has been at Cardiff University, where he now holds a Chair and leads the Visual Computing research group. He is also a guest professor at Tsinghua and two other universities in China, and is a director of Scientific Programmes of the One Wales Research Institute of Visual Computing. His publications include about 300 papers and 15 books covering such topics as solid modelling, surface modelling, reverse engineering, intelligent sketch input, mesh processing, video processing, computer graphics, vision based geometric inspection, and geometric reasoning. He is a Fellow of the Learned Society of Wales, the Institute of Mathematics and its Applications, and the British Computer Society. He is on the editorial boards of Computer-Aided Design, Computer Aided Geometric Design, Geometric Models, and Computational Visual Media.
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Pickup, D., Sun, X., Rosin, P.L. et al. Skeleton-based canonical forms for non-rigid 3D shape retrieval. Comp. Visual Media 2, 231–243 (2016). https://doi.org/10.1007/s41095-016-0045-5
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DOI: https://doi.org/10.1007/s41095-016-0045-5