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Adaptive modeling of dense scattered volumetric and manifold data.
| Content Provider | CiteSeerX |
|---|---|
| Author | Bajaj, Chandrajit Bernardini, Fausto Xu, Guoliang |
| Abstract | In this paper, we propose a technique to approximate a dense set of volumetric scattered scalar values with a C 1 -continuous Bernstein-B'ezier piecewisepolynomial function of low degree. We also present an algorithm to model data scattered over the unknown surface of a 3D object. The manifold data reconstruction problem is reduced to volumetric data modeling, by defining a signeddistance function associated with the set of data points. The algorithm can be used to reconstruct both a model of the surface on which the scattered points lie, and a model of the scalar function defined on the surface (surface-on-surface). Our scheme is capable of adapting to the local level of detail required, using an octree-like domain subdivision scheme. The reconstructed model can be used to visualize and interact with the data in a variety of ways. 1 Introduction In many scientific applications a physical quantity is measured (or computed, in the case of a simulation) Supported in part by NSF gran... |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Adaptive Modeling Manifold Data Dense Scattered Volumetric Data Point Nsf Gran Volumetric Scattered Scalar Value Low Degree Dense Set Scalar Function Reconstructed Model Unknown Surface Octree-like Domain Subdivision Scheme Piecewisepolynomial Function Local Level Scattered Point Lie Signeddistance Function Many Scientific Application Physical Quantity Continuous Bernstein-b Manifold Data Reconstruction Problem |
| Content Type | Text |
