William J. Schroeder

Interactive out-of-core isosurface extraction

We present a novel out-of-core technique for the interactive computation of isosurfaces from volume data. Our algorithm minimizes the main memory and disk space requirements on the visualization workstation, while speeding up isosurface extraction queries. Our overall approach is a two-level indexing scheme. First, by our meta-cell technique, we partition the original dataset into clusters of cells, called meta-cells. Secondly, we produce meta-intervals associated with the meta-cells, and build an indexing data structure on the meta-intervals. We separate the cell information, kept only in meta-cells on disk, from the indexing structure, which is also on disk and only contains pointers to meta-cells. Our meta-cell technique is an I/O-efficient approach for computing a k-d-tree-like partition of the dataset. Our indexing data structure, the binary blocked I/O interval tree, is a new I/O-optimal data structure to perform stabbing queries that report from a set of meta-intervals (or intervals) those containing a query value q. Our tree is simpler to implement, and is also more space-efficient in practice than existing structures. To perform an isosurface query, we first query the indexing structure, and then use the reported meta-cell pointers to read from disk the active meta-cells intersected by the isosurface. The isosurface itself can then be generated from active meta-cells. Rather than being a single cost indexing approach, our technique exhibits a smooth trade-off between query time and disk space.

Visualizing with VTK: a tutorial

We introduce basic concepts behind the Visualization Toolkit (VTK). An overview of the system, plus some detailed examples, will assist in learning this system. The tutorial targets researchers of any discipline who have 2D or 3D data and want more control over the visualization process than a turn-key system can provide. It also assists developers who would like to incorporate VTK into an application as a visualization or data processing engine.

Research Challenges for Visualization Software

As the visualization research community reorients its software to address up-coming challenges, it must successfully deal with diverse processor architectures, distributed systems, various data sources, massive parallelism, multiple input and output devices, and interactivity.

Methods and framework for visualizing higher-order finite elements

The finite element method is an important, widely used numerical technique for solving partial differential equations. This technique utilizes basis functions for approximating the geometry and the variation of the solution field over finite regions, or elements, of the domain. These basis functions are generally formed by combinations of polynomials. In the past, the polynomial order of the basis has been low-typically of linear and quadratic order. However, in recent years so-called p and hp methods have been developed, which may elevate the order of the basis to arbitrary levels with the aim of accelerating the convergence of the numerical solution. The increasing complexity of numerical basis functions poses a significant challenge to visualization systems. In the past, such systems have been loosely coupled to simulation packages, exchanging data via file transfer, and internally reimplementing the basis functions in order to perform interpolation and implement visualization algorithms. However, as the basis functions become more complex and, in some cases, proprietary in nature, it becomes increasingly difficult if not impossible to reimplement them within the visualization system. Further, most visualization systems typically process linear primitives, in part to take advantage of graphics hardware and, in part, due to the inherent simplicity of the resulting algorithms. Thus, visualization of higher-order finite elements requires tessellating the basis to produce data compatible with existing visualization systems. In this paper, we describe adaptive methods that automatically tessellate complex finite element basis functions using a flexible and extensible software framework. These methods employ a recursive, edge-based subdivision algorithm driven by a set of error metrics including geometric error, solution error, and error in image space. Further, we describe advanced pretessellation techniques that guarantees capture of the critical points of the polynomial basis. The framework has been designed using the adaptor design pattern, meaning that the visualization system need not reimplement basis functions, rather it communicates with the simulation package via simple programmatic queries. We demonstrate our method on several examples, and have implemented the framework in the open-source visualization system VTK.

Compatible Triangulations of Spatial Decompositions

We describe a general algorithm to produce compatible 3D triangulations from spatial decompositions. Such triangulations match edges and faces across spatial cell boundaries, solving several problems in graphics and visualization including the crack problem found in adaptive isosurface generation, triangulation of arbitrary grids (including unstructured grids), clipping, and the interval tetrahedrization problem. The algorithm produces compatible triangulations on a cell-by-cell basis, using a modified Delaunay triangulation with a simple point ordering rule to resolve degenerate cases and produce unique triangulations across cell boundaries. The algorithm is naturally parallel since it requires no neighborhood cell information, only a unique, global point numbering. We show application of this algorithm to adaptive contour generation; tetrahedrization of unstructured meshes; clipping and interval volume mesh generation.

Application of path planning and visualization for industrial-design and maintainability-analysis

Software applications Product Vision, and its successor, Galileo, were developed to allow engineers to interactively fly through a digital virtual jet engine, and automatically determine removal paths for maintenance simulations. Three equally significant hurdles have been overcome during the development of this software, with the following results: state-of-the-art algorithms have been developed to find part-removal paths, create swept volumes, and decimate large models for interactive visualization. A powerful yet easy-to-use application has been developed that can efficiently manipulate large databases. The application has been integrated into many aspects of a large industrial design cycle. The application, Galileo, is being actively used by GE Aircraft Engines (GEAE). The visualization and maintainability analysis technology embedded in this software has eliminated the need for costly physical mockups and pushed maintenance issues to the earliest stages of the design cycle. The tool has facilitated concurrent engineering through visually enhanced communications. It has been adopted for aiding the assembly of engines, and for training service mechanics.

The National Alliance for Medical Image Computing, a roadmap initiative to build a free and open source software infrastructure for translational research in medical image analysis

The National Alliance for Medical Image Computing (NA-MIC), is a multi-institutional, interdisciplinary community of researchers, who share the recognition that modern health care demands improved technologies to ease suffering and prolong productive life. Organized under the National Centers for Biomedical Computing 7 years ago, the mission of NA-MIC is to implement a robust and flexible open-source infrastructure for developing and applying advanced imaging technologies across a range of important biomedical research disciplines. A measure of its success, NA-MIC is now applying this technology to diseases that have immense impact on the duration and quality of life: cancer, heart disease, trauma, and degenerative genetic diseases. The targets of this technology range from group comparisons to subject-specific analysis.

Flying edges: A high-performance scalable isocontouring algorithm

Isocontouring remains one of the most widely used visualization techniques. While a plethora of important contouring algorithms have been developed over the last few decades, many were created prior to the advent of ubiquitous parallel computing systems. With the emergence of large data and parallel architectures, a rethinking of isocontouring and other visualization algorithms is necessary to take full advantage of modern computing hardware. To this end we have developed a high-performance isocontouring algorithm for structured data that is designed to be inherently scalable. Processing is performed completely independently along edges over multiple passes. This novel algorithm also employs computational trimming based on geometric reasoning to eliminate unnecessary computation, and removes the parallel bottleneck due to coincident point merging. As a result the algorithm performs well in serial or parallel execution, and supports heterogeneous parallel computation combining data parallel and shared memory approaches. Further it is capable of processing data too large to fit entirely inside GPU memory, does not suffer additional costs due to preprocessing and search structures, and is the fastest non-preprocessed isocontouring algorithm of which we are aware on shared memory, multi-core systems. The software is currently available under a permissive, open source licence in the VTK visualization system.