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Efficient representations for multi-resolution modeling (2006).
| Content Provider | CiteSeerX |
|---|---|
| Researcher | Sobrero, Davide |
| Abstract | Thanks to improvement in simulation, high resolution scanning facilities and multidimensional medical imaging, the size of geometrical dataset is rapidly increasing, and huge datasets are commonly available. For this reason, Level-of-Detail (LOD) techniques have been proposed for triangle-based models of terrains and of the boundary of 3D manifold objects in a variety of applications. The basic operation that an LOD model needs to support is selective refinement. Selective refinement consists of extracting an adaptive mesh-based representation that satisfies some application-dependent requirements. The extracted representation may have a resolution which is uniform or varies in different parts of the shape or of the field domain. In this thesis we address the problem of designing LOD data structures for supporting selective refinement. The emphasis of this work is designing and developing compact LOD data structures tailored to specific applications. Our reference LOD model is the Multi-Tessellation, a dimension-independent model based on a collection of modifications organized according to a dependency relation, which guides the extraction of meshes at |
| File Format | |
| Publisher Date | 2006-01-01 |
| Access Restriction | Open |
| Subject Keyword | Multi-resolution Modeling Efficient Representation Selective Refinement Compact Lod Data Structure Huge Datasets Adaptive Mesh-based Representation Extracted Representation Dimension-independent Model Specific Application Multidimensional Medical Imaging Lod Model Lod Data Structure Field Domain Different Part High Resolution Reference Lod Model Triangle-based Model Basic Operation Application-dependent Requirement Dependency Relation Geometrical Dataset Manifold Object |
| Content Type | Text |
| Resource Type | Thesis |
