An Improved Skeletion-Based Technique For Three-Dimensional Model Segmentation
Three-dimensional (3D) model segmentation has received tremendous attention in recent years, to partition model mesh into meaningful sub-meshes. Hitherto, there is no robust and consistent segmentation method to overcome the problems of under- and over-segmentation for the meaningful components. Man...
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| Format: | Thesis |
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2016
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| Summary: | Three-dimensional (3D) model segmentation has received tremendous attention in recent years, to partition model mesh into meaningful sub-meshes. Hitherto, there is no robust and consistent segmentation method to overcome the problems of under- and over-segmentation for the meaningful components. Many existing methods require either user-input seeding for the number of segments or to apply minima rules to approximate the meaningful components. Some methods excel only in a narrow range of models. Their methods are vague, sensitive to model shape (unstable) and tedious (duplicated processes). Slinky-based segmentation (SBS) with improved skeleton method is proposed in this thesis to automatically and consistently identify meaningful features of a model. The method is robust on any input model shape. The algorithm begins with voxelization and surface-reconstruction on the input model to get rid of the irregular meshes. Laplacian-based Contraction method is adapted to shrink the model into triangular skeleton. |
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