אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

NURBS is one of the typical way of representing geometric data and have been widely used in many areas such as computer graphics, CAGD, robotics. NURBS represents geometric data based on the mathematical form, and thus it requires relatively small memory space compared to the other representations. Compactness of geometric data is crucial in recent computing environments (Network, Mobile, Multi-Core) and NURBS is good candidate to be a geometric representation in such environments. However, at this moment, NURBS is less popular especially in real-time applications because of relatively slow processing time.

In this talk, we present hierarchical data structures and algorithms which accelerate geometric processing on NURBS. Each data structure is designed based on the mathematical properties (curvature, derivative, parametrization) of the model. Our data structures generate most of their information on the fly and they keep minimum data in advance. Our algorithms based on proposed data structures show the performance improvement in the range of 100~1000 times speed up in computing speed compared with previous results.