cvpr cvpr2013 cvpr2013-46 cvpr2013-46-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Bastien Jacquet, Roland Angst, Marc Pollefeys
Abstract: Articulated objects represent an important class ofobjects in our everyday environment. Automatic detection of the type of articulated or otherwise restricted motion and extraction of the corresponding motion parameters are therefore of high value, e.g. in order to augment an otherwise static 3D reconstruction with dynamic semantics, such as rotation axes and allowable translation directions for certain rigid parts or objects. Hence, in this paper, a novel theory to analyse relative transformations between two motion-restricted parts will be presented. The analysis is based on linear subspaces spanned by relative transformations. Moreover, a signature for relative transformations will be introduced which uniquely specifies the type of restricted motion encoded in these relative transformations. This theoretic framework enables the derivation of novel algebraic constraints, such as low-rank constraints for subsequent rotations around two fixed axes for example. Lastly, given the type of restricted motion as predicted by the signature, the paper shows how to extract all the motion parameters with matrix manipulations from linear algebra. Our theory is verified on several real data sets, such as a rotating blackboard or a wheel rolling on the floor amongst others.
[1] S. Agarwal, N. Snavely, I. Simon, S. M. Seitz, and R. Szeliski. Building rome in a day. In ICCV, 2009.
[2] R. Angst and M. Pollefeys. Static multi-camera factorization using rigid motion. In ICCV, 2009.
[3] R. Angst and M. Pollefeys. 5d motion subspaces for planar motions. In ECCV, 2010.
[4] W. Chang and M. Zwicker. Global registration of dynamic range scans for articulated model reconstruction. ACM Transactions on Graphics, 2011.
[5] N. da Silva and J. Costeira. Subspace segmentation with outliers: A grassmannian approach to the maximum consensus subspace. In CVPR, 2008.
[6] E. de Aguiar, C. Theobalt, and H.-P. Seidel. Automatic learning of articulated skeletons from 3d marker trajectories. In ISVC, 2006.
[7] E. Elhamifar and R. Vidal. Sparse subspace clustering: Algorithm, theory, and applications. CoRR, abs/1203. 1005, 2012.
[8] J. Fayad, C. Russell, and L. de Agapito. Automated articulated structure and 3d shape recovery from point correspondences. In ICCV, 2011.
[9] M. A. Fischler and R. C. Bolles. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, pages 381–395, 1981.
[10] R. Hartley and A. Zisserman. Multiple view geometry in computer vision. Cambridge University Press, 2004.
[11] B. Jacquet, R. Angst, and M. Pollefeys. Articulated and restricted motion subspaces and their signatures : Supplemental material. In CVPR, 2013.
[12] D. Katz and O. Brock. Interactive segmentation of articulated objects in 3d. In Workshop on Mobile Manipulation at ICRA, 2011.
[13] A. G. Kirk, J. F. O’Brien, and D. A. Forsyth. Skeletal parameter estimation from optical motion capture data. In CVPR, 2005.
[14] J. F. O’Brien, R. E. Bodenheimer, G. J. Brostow, and J. K. Hodgins. Automatic joint parameter estimation from mag-
[15]
[16]
[17]
[18]
[19]
[20]
[21] netic motion capture data. In Graphics Interface. Canadian Human-Computer Communications Society, 2000. M. Paladini, A. D. Bue, J. M. F. Xavier, L. de Agapito, M. Stosic, and M. Dodig. Optimal metric projections for deformable and articulated structure-from-motion. IJCV, 2012. D. A. Ross, D. Tarlow, and R. S. Zemel. Learning articulated structure and motion. IJCV, 2010. J. Sturm, C. Stachniss, and W. Burgard. A probabilistic framework for learning kinematic models of articulated objects. J. Artif. Intell. Res. (JAIR), 2011. C. Tomasi and T. Kanade. Shape and motion from image streams under orthography: a factorization method. IJCV, 1992. P. A. Tresadern and I. D. Reid. Articulated structure from motion by factorization. In CVPR, 2005. R. Tron and R. Vidal. A benchmark for the comparison of 3-d motion segmentation algorithms. In CVPR, 2007. J. Yan and M. Pollefeys. A factorization-based approach for articulated nonrigid shape, motion and kinematic chain recovery from video. IEEE TPAMI, 2008. 111555 111311