Visualization: Plenoptic Opacity Function for Large Date Visualization

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The gigabytes to terabytes of data now being produced by scientific and medical applications on a routine basis create a pressing demand for capable visualization tools. One of the key problems hindering the development of such tools is the growing disparity between the available system bandwidth and the total need of data access during interactive rendering of these large data sets. Unfortunately, such scarcity of physical bandwidth persists in spite of repeated efforts to accelerate I/O subsystems. Complementary to those continued efforts in increasing I/O performance, this project focuses on intelligently decreasing the need of data access using a unique, coarse-grained method of visibility culling. Visibility culling aims at eliminating unnecessary visualization computation by focusing on parts of the data that are visible from a specific view, culling occluded portions, which may be substantial, in the early stages of the visualization pipeline. Although visibility culling has previously been done as a by-product of the rendering process, it has yet to be explored as an independent technique for attacking the bandwidth bottleneck. For that purpose, the project we propose here will focus on the discovery and development of new visibility culling algorithms that are generic, scalable and efficient. Our key concept is to encode meta-opacity via precomputation. In any volume each voxel has a predetermined value of opacity. During rendering, a sequence of voxels is composited in depth order to form a view-dependent opacity value, called meta-opacity, for each ray segment. We hypothesize that there exists a way to conservatively and efficiently encode meta-opacity of a convex-shaped volume block for a range of views under a family of transfer functions via a one-time pre-computation. We call such encoding the Plenoptic Opacity Function (POF). Using POF for conservative run-time opacity estimation, great accelerations by visibility culling in volume rendering can be accomplished while the transfer function can be modified within the same family on the fly. Here we test this hypothesis and apply POF to volume rendering in large-scale parallelism, as well as out-of-core visualization.