The ACM Symposium on Solid and Physical Modeling is an annual forum which brings together researchers from all over the world to present and discuss new results in creating, analyzing and controlling the shape, structure and behavior of solid objects. This not only includes the traditional design, r
Solid and physical modeling
โ Scribed by Shi-Min Hu; Bruno Levy; Dinesh Manocha
- Publisher
- Elsevier Science
- Year
- 2008
- Tongue
- English
- Weight
- 139 KB
- Volume
- 40
- Category
- Article
- ISSN
- 0010-4485
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โฆ Synopsis
ACM Symposium on Solid and Physical Modeling and Applications (ACM SPM) was held in Beijing, China from June 4 to 6, 2007. ACM SPM is an annual international forum for the exchange of recent research results and applications of spatial modeling and computations in design, analysis and manufacturing, as well as emerging areas such as biomedical, geophysical computing and robotics. This special issue contains improved and extended versions of selected presentations at ACM SPM 2007.
The first paper, by Xia and Wang, presents a level set based method for simultaneous optimization of material property and topology of functionally graded structures; volume fraction and structural boundary are considered as design variables, with the former being discretized as a scalar field and the latter being implicitly represented by the level set method.
The second paper, by Gu, He, Jin, Luo, Qin and Yau, describes a novel computational technique to define and construct manifold splines with only one singular point by employing the rigorous mathematical theory of Ricci flow. The manifold splines are used to extend the algorithmic pipeline of spline surface construction from any planar domain to arbitrary topology.
The third paper, by Emiris and Tzoumas, presents an approach for computing the Voronoi diagram of a set of ellipses with parametric representation under the Euclidean metric. This paper also describes an exact and complete solution for the InCircle predicate.
The fourth paper, by Schall, Belyaev and Seidel, presents a new method for noise removal in static and time-varying range datasets. The proposed approach predicts the restored position of a perturbed vertex based on similar vertices in its neighborhood. It defines the similarity measure in a non-local manner, which compares regions of the surface instead of point pairs, and leads to a more accurate denoising algorithm.
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