| Eurographics
2012 Tutorial (EG Webpage) Will Chang UC San Diego Hao Li Industrial Light & Magic Niloy Mitra UCL London Mark Pauly EPFL Lausanne Michael Wand (organizer) Saarland University / MPI Informatik |
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Abstract
Throughout the last few years, acquisition and processing of dynamic geometry has already received quite an amount of attention in the computer vision and graphics research community. Recently, the topic has gained a significant boost due to the availability of commodity devices for dynamic geometry acquisition: The introduction of the Microsoft “Kinect” device made this kind of technology broadly available, being very well received by both researchers and end-users, and even more development in this direction can probably be expected for the near future. The tutorial on “Dynamic Geometry Processing” considers the problem of processing such dynamic range data effectively and efficiently. The tutorial introduces basic processing techniques for analyzing and matching range data. It introduces models for correspondence estimation and presents the according basic algorithmic building blocks. Furthermore, it discusses the current state-of-the-art by looking at example approaches for processing and real-time tracking of dynamic data. In addition, the tutorial will also identify and discuss future challenges in the field, aiming at inspiring future work in this exciting area of research.
Tutorial Slides Download
- PART I: Introduction
- 1.1 Motivation, Data Sources (Michael)
- 1.2 Basic registration algorithms, ICP (Niloy)
- PART II: Basic Techniques
- 2.1 Dynamic Geometry Registration (Niloy)
- 2.2 Incremental Deformable Matching (Michael)
- 2.3 Deformation Graphs (Mark)
- Part III: State-of-the-Art Example Systems
Data Sets
This is a collection of publically available data sets with real-time captures of dynamic geometry. If you are aware of a link that is missing, drop us an email.
- König et al.'s motion compensated structured light scans at TU Dresden: repository page.
- Weise et al.'s motion compensated structured light scans at ETH Zürich: repository page.
- Vlasic et al.'s data photometric acquisition resutls at MIT: repository page.
- Bradley et al.'s markerless garmet capture at UBC: repository page.
- Solid Works Cosmic Blob test sequence at MIT: repository page.
- Allen et al.'s human body scans at University of Washington: repository page.
- Sumner et al.'s deformation transfer mesh data at MIT: repository page.
- Pekelny and Gotsman's articulated object scans: download
(thanks to the authors for the permission for hosting the data).
The data set is taken from the following publication: Yuri Pekelny and Craig Gotsman: Articulated Object Reconstruction and Markerless Motion Capture from Depth Video. Computer Graphics Forum 27(2) (Eurographics), 2008.
References
This is a collection of references mentioned during the tutorial. The list is not indented to be exhaustive, only reflecting what we talked about during the tutorial.
Local Registration / Deformation Modeling
- Allen, B., Curless, B., Popovic, Z.: The space of human body shapes: reconstruction and parameterization from range scans. In: ACM Trans. Graphics (Proc. Siggraph 2003), pp. 587–594, 2003.
- Brown, B., Rusinkiewicz, S.: Global non-rigid alignment of 3-d scans. In: ACM Transactions on Graphics (Proc. Siggraph 2007) 26, 3, 2007.
- Häehnel, D., Thrun, S., Burgard, W.: An extension of the icp algorithm for modeling nonrigid objects with mobile robots. In: Proc. Int. Joint Conf. on Artificial Intelligence (IJCAI), 2003.
- Li, H., Sumner, R.W., Pauly, M.: Global correspondence optimization for non-rigid registration of depth scans. In: Computer Graphics Forum (Proc. SGP 2008), 27, 5, 2008.
Global Registration
- Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., Davis, J.: Scape: shape completion and animation of people. In: ACM Trans. Graph. 24, 3 (Proc. Siggraph 2005), 408–416, 2005.
- Anguelov, D., Srinivasan, P., Pang, H.-C., Koller, D., Thrun, S., Davis, J.: The correlated correspondence algorithm for unsupervised registration of nonrigid surfaces. In: Proc. Neural Information Processing Systems (NIPS), 2004.
- Chang, W., Zwicker, M.: Range scan registration using reduced deformable models. In: Computer Graphics Forum (Proc. Eurgraphics 2009) 28, 2, 447–456, 2009.
- Huang, Q.-X., Adams, B., Wicke, M., Guibas, L. J.: Non-rigid registration under isometric deformations. In: Computer Graphics Forum (Proc. SGP 2008), 27, 5, 2008.
- Johnson, A. E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3d scenes. In: IEEE Trans. Pattern Anal. Mach. Intell. 21, 433–449, 1999.
- Lipman
Y., Funkhouser T.: Möbius voting for surface
correspondence. In: ACM
Transactions on Graphics 28(3) (Proc. Siggraph)
, 2009.
- Huang, Q.-X., Adams, B., Wand, M.: Bayesian surface reconstruction via
iterative scan alignment to an optimized prototype. In: Proc. 5th
Eurographics Symposium on Geometry Processing, 2007.
- Huber, D., Hebert, M.: Fully automatic registration of multiple
3d data sets. In: IEEE Computer Society Workshop on Computer Vision
Beyond the Visible Spectrum (CVBVS 2001), December 2001.
- Li, H., Adams, B., Guibas, L. J., Pauly,
M.: Robust single-view geometry and motion
reconstruction. In: ACM Trans. Graph. 28, 5, 2009.
- Mitra, N. J., Flory, S., Ovsjanikov, M.,
Gelfand, N., Guibas, L., Pottmann, H.: Dynamic geometry registration. In: Symposium on Geometry
Processing, pp. 173–182, 2007.
- Pekelny, Y., Gotsman, C.: Articulated Object Reconstruction and Markerless
Motion Capture from Depth Video. Computer Graphics Forum 27(2) (Eurographics), 2008.
- Wand, M., Adams, B., Ovsjanikov, M.,
Berner, A., Bokeloh, M., Jenke, P., Guibas, L., Seidel, H.-P., Schilling,
A.: Efficient reconstruction of nonrigid shape
and motion from real-time 3d scanner data. In: ACM Trans. Graph. 28, 2, 2009.
- Wand, M., Jenke, P., Huang, Q., Bokeloh,
M., Guibas, L., Schilling, A.: Reconstruction of deforming geometry from time-varying point clouds.
In: SGP ’07: Proceedings of the fifth Eurographics Symposium on
Geometry Processing, pp. 49–58, 2007.
- Davis, J., Nehab, D., Ramamoorthi, R., Rusinkiewicz, S.: Spacetime stereo: A unifying framework for depth from triangulation. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 27, 2, 296–302, Feb. 2005.
- Weise, T., Leibe, B., Gool, L. J. V.: Fast 3d scanning with automatic motion compensation. In: IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2007.
- Zitnick,
C. L., Kang, S. B., Uyttendaele, M., Winder, S., Szeliski, R.:
High-quality video
view interpolation using a layered representation. In: ACM Trans. on Graphics 23, 3,
600–608, 2004.
- Zhang, L., Snavely, N., Curless, B., Seitz, S. M.: Spacetime faces: high resolution capture for modeling and animation. In: ACM Trans. Graphics (Proc. Siggraph 2004), pp. 548–558, 2004.