2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization &Amp; Transmission 2012
DOI: 10.1109/3dimpvt.2012.30
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An Adaptive Hierarchical Approach to the Extraction of High Resolution Medial Surfaces

Abstract: We introduce a novel algorithm for medial surfaces extraction that is based on the density-corrected Hamiltonian analysis [20]. The approach extracts the skeleton directly from a triangulated mesh and adopts an adaptive octreebased approach in which only skeletal voxels are refined to a lower level of the hierarchy, resulting in robust and accurate skeletons at extremely high resolution. The quality of the extracted medial surfaces is confirmed by an extensive set of experiments.

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Cited by 5 publications
(6 citation statements)
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“…Torsello and Rossi extend the TH density advection to extract 3D medial surfaces [54]. In their model, density advection stops when reaching the surface skeleton.…”
Section: B Comparison With Hamiltonian Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Torsello and Rossi extend the TH density advection to extract 3D medial surfaces [54]. In their model, density advection stops when reaching the surface skeleton.…”
Section: B Comparison With Hamiltonian Methodsmentioning
confidence: 99%
“…Torsello and Hancock correct this for a more accurate 2D skeleton detection by a momentum conservation principle ∇ · (ρ∇DT ∂ Ω ) = 0, where ρ is the mass density on the evolving boundary ∂ Ω [5]. Rossi and Torsello extend this idea to compute 3D surface skeletons [54]. However, this method does not compute curve skeletons and does not model the curve-surface skeleton relationship.…”
Section: Introductionmentioning
confidence: 99%
“…Advection method: Several methods compute 3D skeletons by contracting the surface ∂ in a vector field v that simulates advection of uniformly-spread mass from ∂ following a momentum conservation principle [54,65] . We use here the field v proposed in [54] , which has the desirable properties of being tangent to the skeleton surface and pointing towards its core.…”
Section: Problem A: Removing Noise Close To Shape Featuresmentioning
confidence: 99%
“…Thinning is simple and fast, but can be sensitive to rigid Euclidean transformations. Field methods find S along singularities of DT ∂ or related fields [17,20,23,39,40,43,53] and can be efficiently implemented on GPUs [6,49] . Mesh methods use a surface sampling of ∂ only, which is cheaper and faster than the volumetric sampling of used by field and thinning methods.…”
Section: Computing Medial Surfaces and Their Featuresmentioning
confidence: 99%