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Conference Paper

Statistical Learning for Shape Applications

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45337

Saleem,  Waqar
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45696

Wang,  Danyi
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44112

Belyaev,  Alexander
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45449

Seidel,  Hans-Peter
Computer Graphics, MPI for Informatics, Max Planck Society;

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Citation

Saleem, W., Wang, D., Belyaev, A., & Seidel, H.-P. (2006). Statistical Learning for Shape Applications. In 1st International Symposium on Shapes and Semantics (pp. 53-60). Genova, Italy: CNR.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-2402-6
Abstract
Statistical methods are well suited to the large amounts of data typically involved in digital shape applications. In this paper, we look at two statistical learning methods related to digital shape processing. The first, \textit{neural meshes}, learns the shape of a given point cloud \--- the surface reconstruction problem \--- in $O(n^2)$ time. We present an alternate implementation of the algorithm that takes $O(n\log n)$ time. Secondly, we present a simple method to automatically learn the correct orientation of a shape in an image from a database of images with correctly oriented shapes.