NDLI: A framework for multi-dimensional adaptive subdivision objects

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A framework for multi-dimensional adaptive subdivision objects

Content Provider	ACM Digital Library
Author	Qin, Hong Chang, Yu-Sung
Abstract	Despite the growing interest in subdivision surfaces within the computer graphics and geometric processing communities, subdivision approaches have been receiving much less attention in solid modeling. This paper presents a powerful new framework for a subdivision scheme that is defined over a simplicial complex in any n-D space. We first present a series of definitions to facilitate topological inquiries during the subdivision process. The scheme is derived from the double (k + 1)-directional box splines over k-simplicial domains. Thus, it guarantees a certain level of smoothness in the limit on a regular mesh. The subdivision rules are modified by spatial averaging to guarantee $C^{1}$ smoothness near extraordinary cases. Within a single framework, we combine the subdivision rules that can produce 1-, 2-, and 3-manifold in arbitrary n-D space. Possible solutions for non-manifold regions between the manifolds with different dimensions are suggested as a form of selective subdivision rules according to user preference. We briefly describe the subdivision matrix analysis to ensure a reasonable smoothness across extraordinary topologies, and empirical results support our assumption. In addition, through modifications, we show that the scheme can easily represent objects with singularities, such as cusps, creases, or corners. We further develop local adaptive refinement rules that can achieve level-of-detail control for hierarchical modeling. Our implementation is based on the topological properties of a simplicial domain. Therefore, it is flexible and extendable. We also develop a solid modeling system founded on our theoretical framework to show potential benefits of our work in industrial design, geometric processing, and other applications.
Starting Page	123
Ending Page	134
Page Count	12
File Format	PDF
ISBN	390567355X
ISSN	18117783
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-06-09
Publisher Place	Goslar
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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