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.

Techniques in numerical simulation such as the finite element method depend on basis functions for approximating the geometry and variation of the solution over discrete regions of a domain. Existing visualization systems can visualize these basis functions if they are linear, or for a small set of simple non-linear bases. However, newer numerical approaches often use basis functions of elevated and mixed order or complex form; hence existing visualization systems cannot directly process them. In this paper we describe an approach that supports automatic, adaptive tessellation of general basis functions using a flexible and extensible software architecture in conjunction with an on demand, edge-based recursive subdivision algorithm. The framework supports the use of functions implemented in external simulation packages, eliminating the need to reimplement the bases within the visualization system. We demonstrate our method on several examples, and have implemented the framework in the open-source visualization system VTK.

We propose a new approach to the problem of generating a simple topologically-closed geometric model from a point-sampled volume data set. We call such a model a Geometrically Deformed Model or GDM. A GDM is created by placing a 'seed' model in the volume data set. The model is then deformed by a relaxation process that minimizes a set of constraints that provides a measure of how well the model fits the features in the data. Constraints are associated with each vertex in the model that control local deformation, interaction between the model and the data set, and the shape and topology of the model. Once generated, a GDM can be used for visualization, shape recognition, geometric measurements, or subjected to a series of geometric operations. This technique is of special importance because of the advent of nondestructive sensing equipment (CT, MRI) that generates point samples of true three-dimensional objects.

Modeling clip gives an application the ability to remove sections of an object in order to view internal detail. The clipping volume defied by modeling clip can be concave and disjoint, and is composed of a set of volumes that are specified in modeling coordinates. The modeling clip functionality has been included in the PHIGS specification [4], Some interesting peculiarities arise from the fact that most graphics pipelines (such as PHIGS) are algebraically based and that modeling clip regions are specified in modeling coordinates. One such peculiarity occurs when the transformation relating the coordinate system of the clip region to world coordinates is singular. A study on the algorithmic and architectural issues of implementing modeling clip is presented. The resulting algorithm to implement the modeling clip mechanism represents the clip volume as a pipeline of filters with each filter representing one of the sub-volumes. The method handles all of the sixteen possible set combinations between two regions in space. The effects of transformations on modeling clip have been examined, and has resulted in identifying when modeling clip can be efficiently performed in device coordinates as well as the cases when it can not. When handling singular modeling transformations, it is shown that it i