Efficient Bounding Volume Hierarchies for Hair Simulation

نویسندگان

  • G. Sobottka
  • A. Weber
چکیده

We study the practicability and performance of different classes of bounding volume hierarchies for self collision detection in complex human hair styles with authentic geometrical properties. In particular, we focus on topology based hierarchies which allow for fast update schemes as well as conventional spatially adapted hierarchies. In this context, we introduce a parameterized k-DOP approach based on Grassmanian spaces which allows for a systematic generation of normal sets for arbitrary k. This enables us to empirically determine optimal values of k for collision detection based on DOP hierarchies.

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تاریخ انتشار 2005