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CW 381
Ares Lagae, Olivier Dumont, Philip Dutré
Geometry Synthesis
Abstract
Inspired by texture synthesis techniques, we present in this paper a method for geometry synthesis. Given an example of input geometry, we synthesize new output geometry that is perceived similar to the input geometry, but at the same time differs in its local appearance. We assume our input geometry satisfies the constraints of a Markov Random Field model, and represent the input by a hierarchical distance field. This allows us to perform fast matching queries between a target distance field that is partially synthesized, and the input distance field. Once the target distance field is completed, we copy the original corresponding geometry elements to our synthesized result. We show that automatically generating geometry by example can be achieved within reasonable computing times, and is able to produce convincing results.
report.pdf (543K) / mailto: A. Lagae