Resumo: The need to visualize unstructured volumetric data arises in a broad spectrum of applications including structural dynamics, structural mechanics, thermodynamics, fluid mechanics, and shock physics.

In this talk, I will focus on direct volume rendering, a term used to define a particular set of rendering techniques which avoids generating intermediary (surface) representations of the volume data. Instead, the scalar field is generally modeled as a cloud-like material, and rendered by computing a set of lighting equations.

In the first part of the talk, I will concentrate on projective methods. Direct volume rendering based on projective methods works by projecting, in visibility order, the polyhedral cells of a mesh onto the image plane, and incrementally compositing the cell's color and opacity into the final image. Crucial to this method is the computation of a visibility ordering of the cells. I will discuss some recent work on fast polyhedral cell sorting.

In the second part of the talk, I will address the problem of rendering large unstructured volumetric grids on machines with limited memory. This problem is particularly interesting because such datasets are likely to arise from computations generated on supercomputers, that is, machines with superior resources to even the most powerful workstations. I will present a set of techniques which can be used to render arbitrarily large datasets on machines with small memory.
This work was done partly in collaboration with J. Comba (Stanford), R. Farias (Stony Brook), J. Klosowski (IBM T.J. Watson), N. Max (LLNL), J. Mitchell (Stony Brook), and P. Williams (LLNL).

Short bio: Claudio Silva is a Senior Member of Technical Staff in the Information Visualization Research Department at AT&T Research. Before joining AT&T, Claudio was a Research Staff Member at IBM T. J. Watson Research Center. He got his PhD in computer science at the State University of New York at Stony Brook in 1996. While a student, and later as an National Science Foundation (NSF) post-doc, he worked at Sandia National Labs, where he developed large-scale scientific visualization algorithms and tools for handling massive datasets. His main research interests are in graphics, visualization, applied computational geometry, and high-performance computing. He has published over 30 papers in international conferences and journals, and presented courses at various conferences, including ACM Siggraph, Eurographics, and IEEE Visualization.