Reconstructing the Curve-Skeletons of 3D Shapes Using the Visual Hull

Curve-skeletons are the most important descriptors for shapes, capable of capturing in a synthetic manner the most relevant features. They are useful for many different applications: from shape matching and retrieval, to medical imaging, to animation. This has led, over the years, to the development...

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Bibliographic Details
Published in:	IEEE transactions on visualization and computer graphics Vol. 18; no. 11; pp. 1891 - 1901
Main Authors:	Livesu, M., Guggeri, F., Scateni, R.
Format:	Journal Article
Language:	English
Published:	United States IEEE 01-11-2012 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Animals Approximation methods Cameras Clouds Computer Graphics Curve-skeleton Depth Perception Hulls Hulls (structures) Humans Imaging, Three-Dimensional - methods Mathematical models Shape Skeleton Stems stereoscopic vision Studies Three dimensional Three dimensional displays Visual visual hull Visualization
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Summary:	Curve-skeletons are the most important descriptors for shapes, capable of capturing in a synthetic manner the most relevant features. They are useful for many different applications: from shape matching and retrieval, to medical imaging, to animation. This has led, over the years, to the development of several different techniques for extraction, each trying to comply with specific goals. We propose a novel technique which stems from the intuition of reproducing what a human being does to deduce the shape of an object holding it in his or her hand and rotating. To accomplish this, we use the formal definitions of epipolar geometry and visual hull. We show how it is possible to infer the curve-skeleton of a broad class of 3D shapes, along with an estimation of the radii of the maximal inscribed balls, by gathering information about the medial axes of their projections on the image planes of the stereographic vision. It is definitely worth to point out that our method works indifferently on (even unoriented) polygonal meshes, voxel models, and point clouds. Moreover, it is insensitive to noise, pose-invariant, resolution-invariant, and robust when applied to incomplete data sets.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Article-1 ObjectType-Feature-2
ISSN:	1077-2626 1941-0506
DOI:	10.1109/TVCG.2012.71