Abstract
The difficulty of monocular non-rigid 3D reconstruction using statistical learning approaches is to get a model that can represent as many deformations as possible. Given a known dataset to learn a model, existing latent variable models (LVMs) fail to focus on how to attain labeled samples. In this paper, we propose novel clustering-based LVMs in which we automatically select representative samples to be the labeled ones. To this end, G-means algorithm is adopted to cluster latent variables and obtain the labeled samples. These labeled samples are corresponding to the latent variables closest to clustering centers. We learn the Gaussian Process Latent Variable Model (GPLVM) and the Constrained Latent Variable Model (CLVM) into which we introduce clustering in the context of monocular non-rigid 3D reconstruction, and compare them to those without clustering. The experimental results show that our clustering-based LVMs could perform 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
McInerney, T., Terzopoulos, D.: A Finite Element Model for 3D Shape Reconstruction and Nonrigid Motion Tracking. In: Proceedings of 4th IEEE International Conference on Computer Vision, May 11-14, pp. 518–523 (1993)
Tsap, L.V., Goldof, D.B., Sarkar, S.: Nonrigid Motion Analysis Based on Dynamic Refinement of Finite Element Models. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-22(5), 526–543 (2000)
Cohen, L.D., Cohen, I.: Deformable Models for 3-D Medical Images Using Finite Elements and Balloons. In: 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 15-18, pp. 592–598 (1992)
Bregler, C., Hertzmann, A., Biermann, H.: Recovering Non-Rigid 3D Shape from Image Streams. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 13-15, pp. 690–696 (2000)
Taylor, J., Jepson, A.D., Kutulakos, K.N.: Non-Rigid Structure from Locally-Rigid Motion. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 13-18, pp. 2761–2768 (2010)
Garg, R., Roussos, A., Agapito, L.: Dense Variational Reconstruction of Non-Rigid Surfaces from Monocular Video. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 23-28, pp. 1272–1279 (2013)
Salzmann, M., Urtasun, R., Fua, P.: Local deformation models for monocular 3D shape recovery. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 23-28, pp. 1–8 (2008)
Salzmann, M., Fua, P.: Linear Local Models for Monocular Reconstruction of Deformable Surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI 33(5), 931–944 (2011)
Varol, A., Shaji, A., Salzmann, M., Fua, P.: Monocular 3D Reconstruction of Locally Textured Surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI 34(6), 1118–1130 (2012)
Varol, A., Salzmann, M., Fua, P., Urtasun, R.: A constrained latent variable model. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 16-21, pp. 2248–2255 (2012)
Lawrence, N.D.: Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data. In: 17th Annual Conference on Neural Information Processing Systems, NIPS (December 8, 2003), In: Advances in Neural Information Processing Systems, NIPS 2004, vol. 16, pp. 329–336 (2004)
Lawrence, N.D.: Probabilistic non-linear principal component analysis with Gaussian process latent variable models. The Journal of Machine Learning Research 6, 1783–1816 (2005)
Hamerly, G., Elkan, C.: Learning the k in k-means. In: 17th Annual Conference on Neural Information Processing Systems, NIPS 2003 (December 8, 2003), In: Advances in Neural Information Processing Systems, NIPS 2004, vol. 16, pp. 281–288 (2004)
Urtasun, R., Fleet, D.J., Geiger, A., Popović, J., Darrell, T.J., Lawrence, N.D.: Topologically-constrained latent variable models. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1080–1087. ACM, New York (2008)
Salzmann, M., Fua, P.: Deformable surface 3D reconstruction from monocular images. Synthesis Lectures on Computer Vision 2(1), 1–113 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, Q., Wang, F., Li, D., Wang, X. (2014). Clustering-Based Latent Variable Models for Monocular Non-rigid 3D Shape Recovery. In: Huang, DS., Jo, KH., Wang, L. (eds) Intelligent Computing Methodologies. ICIC 2014. Lecture Notes in Computer Science(), vol 8589. Springer, Cham. https://doi.org/10.1007/978-3-319-09339-0_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-09339-0_17
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09338-3
Online ISBN: 978-3-319-09339-0
eBook Packages: Computer ScienceComputer Science (R0)