Computational Schemes for Biomimetic Sculpture

B. Hatcher, K. Aspelund, A. Willis, J. Speicher, D.B. Cooper & F.F. Leymarie
Proceedings of the 5th International Conference on Creativity and Cognition, ACM,
pp. 22-31 (Exhibition: pp.298-300), London, U.K., April 2005.

Abstract

A prototype system for the automatic evolution of biomimetic structures using structural automata is described and its utility for generating digital sculpture is demonstrated. Sculptures are generated from a primordial shape which is represented in terms of a triangular mesh and sculpture is created by extending the original surface using tetrahedral structural elements. Recursively applicable rules or equivalently, automata, are defined which allow the sculptor to generate a volumetric scaffold from the original surface. This scaffold is generated using the stated rules for inserting and connecting together the tetrahedral elements. The software is operated as a generative process where sculptures are grown from an original triangular surface mesh as a sequence of layers. Each layer is created as a 2-step process. In step 1, we populate the surface with tetrahedral structures where the base of each tetrahedron coincides with a surface triangle. Step 2 re-triangulates the apexes of the tetrahedra from step 1 creating an offset and deformed version of the original surface mesh. The sculptor has artistic control of the process at all points and may assign or change rules to generate different biomimetic behaviors, i.e., structures which tend to replicate natural phenomena.

Surface Sculpting with Stochastic Deformable 3D Surfaces

Andrew Willis, Jasper J. Speicher and David B. Cooper
Proceeding of the 17th International Conference on Pattern Recognition (ICPR),
Vol. 2, pp. 249-252, Cambridge, U.K.

Abstract

This paper introduces a new stochastic surface model for deformable 3D surfaces and demonstrates its utility for the purpose of 3D sculpting. This is the problem of simple-to-use and intuitively interactive 3D free-form model building. A 3D surface is a sample of a Markov Random Field (MRF) defined on the vertices of a 3D mesh where MRF sites coincide with mesh vertices and the MRF cliques consist of subsets of sites. Each site has 3D coordinates (x,y,z) as random variables and is a member of one or more clique potentials which are functions of the vertices in a clique and describe stochastic dependencies among sites. Data, which is used to deform the surface can consist of, but is not limited to, an unorganized set of 3D points and is modeled by a conditional probability distribution given the 3D surface. A deformed surface is a MAP (Maximum A posteriori Probability) estimate of the joint distribution of the MRF surface model and the data. The generality and simplicity of the MRF model provides the ability to incorporate unlimited local and global deformation properties. Included in our development is the introduction of new data models, new anisotropic clique potentials, and cliques which involve sites that are spatially far apart. Other applications of these models are possible, e.g., stereo reconstruction.

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