Volume 6, Issue 1
Geometric and Photometric Data Fusion in Non-Rigid Shape Analysis

Artiom Kovnatsky, Dan Raviv, Michael M. Bronstein, Alexander M. Bronstein & Ron Kimmel

Numer. Math. Theor. Meth. Appl., 6 (2013), pp. 199-222.

Published online: 2013-06

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  • Abstract

In this paper, we explore the use of the diffusion geometry framework for the fusion of geometric and photometric information in local and global shape descriptors. Our construction is based on the definition of a diffusion process on the shape manifold embedded into a high-dimensional space where the embedding coordinates represent the photometric information. Experimental results show that such data fusion is useful in coping with different challenges of shape analysis where pure geometric and pure photometric methods fail.

  • Keywords

Laplace-Beltrami operator, diffusion equation, heat kernel descriptors, 3D shape retrieval, deformation invariance.

  • AMS Subject Headings

65M10, 78A48

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{NMTMA-6-199, author = {}, title = {Geometric and Photometric Data Fusion in Non-Rigid Shape Analysis}, journal = {Numerical Mathematics: Theory, Methods and Applications}, year = {2013}, volume = {6}, number = {1}, pages = {199--222}, abstract = {

In this paper, we explore the use of the diffusion geometry framework for the fusion of geometric and photometric information in local and global shape descriptors. Our construction is based on the definition of a diffusion process on the shape manifold embedded into a high-dimensional space where the embedding coordinates represent the photometric information. Experimental results show that such data fusion is useful in coping with different challenges of shape analysis where pure geometric and pure photometric methods fail.

}, issn = {2079-7338}, doi = {https://doi.org/10.4208/nmtma.2013.mssvm11}, url = {http://global-sci.org/intro/article_detail/nmtma/5900.html} }
TY - JOUR T1 - Geometric and Photometric Data Fusion in Non-Rigid Shape Analysis JO - Numerical Mathematics: Theory, Methods and Applications VL - 1 SP - 199 EP - 222 PY - 2013 DA - 2013/06 SN - 6 DO - http://doi.org/10.4208/nmtma.2013.mssvm11 UR - https://global-sci.org/intro/article_detail/nmtma/5900.html KW - Laplace-Beltrami operator, diffusion equation, heat kernel descriptors, 3D shape retrieval, deformation invariance. AB -

In this paper, we explore the use of the diffusion geometry framework for the fusion of geometric and photometric information in local and global shape descriptors. Our construction is based on the definition of a diffusion process on the shape manifold embedded into a high-dimensional space where the embedding coordinates represent the photometric information. Experimental results show that such data fusion is useful in coping with different challenges of shape analysis where pure geometric and pure photometric methods fail.

Artiom Kovnatsky, Dan Raviv, Michael M. Bronstein, Alexander M. Bronstein & Ron Kimmel. (2020). Geometric and Photometric Data Fusion in Non-Rigid Shape Analysis. Numerical Mathematics: Theory, Methods and Applications. 6 (1). 199-222. doi:10.4208/nmtma.2013.mssvm11
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