IEEE Trans Vis Comput Graph - Reconstructing the Curve-Skeletons of 3D Shapes Using the Visual Hull.

Tópicos

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Resumo

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.

Resumo Limpo

curveskeleton import descriptor shape capabl captur synthet manner relev featur use mani differ applic shape match retriev medic imag anim led year develop sever differ techniqu extract tri compli specif goal propos novel techniqu stem intuit reproduc human deduc shape object hold hand rotat accomplish use formal definit epipolar geometri visual hull show possibl infer curveskeleton broad class d shape along estim radii maxim inscrib ball gather inform medial axe project imag plane stereograph vision definit worth point method work indiffer even unori polygon mesh voxel model point cloud moreov insensit nois poseinvari resolutioninvari robust appli incomplet data set

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