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  Dynamic Probabilistic Volumetric Models

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.

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 Creators:
Ulusoy, Osman1, Author           
Biris, Octavian, Author
Mundy, Joseph L., Author
Affiliations:
1Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497642              

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 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.

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 Dates: 2013-11
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Ulusoy2013
DOI: 10.1109/ICCV.2013.68
 Degree: -

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Title: 2013 IEEE International Conference on Computer Vision (ICCV 2013)
Place of Event: Sydney, Australia
Start-/End Date: 2013-12-01 - 2013-12-08

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Title: 2013 IEEE International Conference on Computer Vision (ICCV 2013)
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 505 - 512 Identifier: ISSN: 1550-5499