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Conference Paper

Dynamic Probabilistic Volumetric Models

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Ulusoy,  Osman
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

Ulusoy, O., Biris, O., & Mundy, J. L. (2013). Dynamic Probabilistic Volumetric Models. In 2013 IEEE International Conference on Computer Vision (ICCV 2013) (pp. 505-512). IEEE. doi:10.1109/ICCV.2013.68.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0024-E356-8
Abstract
This paper presents a probabilistic volumetric framework for image based modeling of general dynamic 3-d scenes. The framework is targeted towards high quality modeling of complex scenes evolving over thousands of frames. Extensive storage and computational resources are required in processing large scale space-time (4-d) data. Existing methods typically store separate 3-d models at each time step and do not address such limitations. A novel 4-d representation is proposed that adaptively subdivides in space and time to explain the appearance of 3-d dynamic surfaces. This representation is shown to achieve compression of 4-d data and provide efficient spatio-temporal processing. The advances of the proposed framework is demonstrated on standard datasets using free-viewpoint video and 3-d tracking applications.