Point-based methods have a long history in graphics for rendering, but their use in modeling and simulation is more recent. Shape representations based on sampled points faithfully reflect several 3-D acquisition technologies and point-based techniques can provide a flexible representation of geometry for situations where forming and maintaining a complete mesh can be cumbersome or complex. This talk will briefly describe some current work in simulating wide area contacts, large-scale deformation and fracture, collision-detection, and qualitative shape analysis of point-sampled data – all carried out using meshless methods. The irregular and dynamic sampling these applications require creates new challenges and leads to methods with a distinctly more combinatorial and topological character.
Leonidas Guibas obtained his Ph.D. from Stanford in 1976, under the supervision of Donald Knuth. His main subsequent employers were Xerox PARC, MIT, and DEC/SRC. He has been at Stanford since 1984 as Professor of Computer Science, where he heads the Geometric Computation group within the Graphics Laboratory. He is also part of the AI Laboratory and the Bio-X Program. Professor Guibas’ interests span computational geometry, geometric modeling, computer graphics, computer vision, robotics, ad hoc communication and sensor networks, and discrete algorithms — all areas in which he has published and lectured extensively. At Stanford he has developed new courses in algorithms and data structures, geometric modeling, geometric algorithms, sensor networks, and biocomputation. Professor Guibas is an ACM Fellow.