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Abstract

Jyh-Ming Lien, Nancy M. Amato, "Approximate Convex Decomposition," Technical Report, TR03-001, Department of Computer Science and Engineering, Texas A&M University, Jan 2003.
Technical Report(ps, pdf, abstract)

One common strategy for dealing with large, complex models is to partition them into pieces that are easier to handle. While decomposition into convex components results in pieces that are easy to process, such decompositions can be costly to construct and often result in representations with an unmanageable number of components. In this paper, we propose an alternative partitioning strategy that decomposes a given polyhedron into “approximately convex" pieces. For many applications, the approximately convex components of this decomposition provide similar benefits as convex components, while the resulting decomposition is both significantly smaller and can be computed more efficiently. Indeed, for many models, an approximate convex decomposition can more accurately represent the important structural features of the model by providing a mechanism for ignoring insignificant features, such as wrinkles and other surface texture. We propose a simple algorithm to compute approximate convex decompositions of polyhedra of arbitrary genus to within a user specified tolerance. This algorithm measures the significance of the model's features and resolves them in order of priority. As a by product, it also produces an elegant hierarchical representation of the model. We illustrate its utility in constructing an approximate skeleton of the model that results in significant performance gains over skeletons based on an exact convex decomposition.