In recent years, there has been increasing research in the field of three-dimensional (3D) computer graphics (CG) simulation in conjunction with CG development, as well as a demand for CG expressions related to food. This paper proposes a CG simulation method for the interaction between pasta (spaghetti) and pasta sauce. Simulating the interaction between noodles as string-like objects and pasta sauce as non-Newtonian fluids is difficult due to their mutually complex behaviors. In this paper, the noodles and the sauce are simulated through an elastic body simulation method called Vellum and the Fluid-Implicit-Particle (FLIP) method, respectively. Thereafter, these methods are combined to express the interaction between the noodles and the sauce.

This paper describes an extendable graphical framework, aflak , which provides a visualization and provenance management environment for the analysis of multi-spectral astronomical datasets. Via its node editor interface, aflak allows the astronomer to compose transforms on input datasets queryable from public astronomical data repositories, then to export the results of the analysis as Flexible Image Transport System (FITS) files, in a manner such that the full provenance of the output data be preserved and reviewable, and that the exported file be usable by other common astronomical analysis software. FITS is the standard of data interchange in astronomy. By embedding aflak ’s provenance data into FITS files, we both achieve interoperability with existing software and full reproducibility of the process by which astronomers make discoveries.

Multi-objective optimization tools have been applied in various academic and industry fields. It is often difficult to optimize all the objectives since they often cause trade-offs. It is also difficult to figure what kinds of trade-offs actually cause. We think visualization of multi-objective optimization results assists users to intuitively understand the distributions of their solutions. This paper proposes a visualization of explanatory variable and objective function spaces in the separate views, so that users can easily understand their relevancy. Also, our tool features the linkage mechanism between the two views. When a user selects certain ranges of the values by a mouse click operation, the tool highlights all the corresponding individuals. This mechanism is useful for users to narrow down the results. We expect the tool assists the understanding of the behavior of the optimization processes and improvement of the future processes.

We have developed a hybrid wind tunnel, where 2D measurement-integrated (MI) simulation, which utilizes actual data acquired from real air flow behind a square cylinder, plays an important role in improving the accuracy of the numerical analysis. The wind tunnel requires an accompanying visual analysis tool with which we can effectively peer into the relationships between the actual and simulated flow fields. In this paper, we attempt to exploit an augmented reality display to that end. The basic idea is to superimpose the computationally-visualized MI simulated pressure field onto the actual flow velocity structure physically-visualized with oil misttraced streaklines instantaneously. Spatial registration of these two visual sources is rather straight-forward since the fixed cylinder of the wind tunnel is easily identified, whereas visualizing the MI simulated pressure field can be characterized with a sophisticated scheme based on differential topology. Considering the fact that vortex centers are located at local minima in the pressure field, and each minimum is surrounded by a derived topological feature called ridge cycle, we can colorize the field adaptively and keep track of Karman vortex streets robustly, regardless of drastic change in the Reynolds number of the flow field.

Multiple-objective optimization problems have multiple objective functions to be optimized simultaneously. It is often difficult to optimize all the objectives since they often cause trade-offs. We produce a visualization tool which displays both explanatory variables and objective functions for multi-objective optimization.

Optimal viewpoint selection is an important task because it considerably influences the amount of information contained in the 2D projected images of 3D objects, and thus dominates their first impressions from a psychological point of view. Although several methods have been proposed that calculate the optimal positions of viewpoints especially for 3D surface meshes, none has been done for solid objects such as volumes. This paper presents a new method of locating such optimal viewpoints when visualizing volumes using direct volume rendering. The major idea behind our method is to decompose an entire volume into a set of feature components, and then find a globally optimal viewpoint by finding a compromise between locally optimal viewpoints for the components. As the feature components, the method employs interval volumes and their combinations that characterize the topological transitions of isosurfaces according to the scalar field. Furthermore, opacity transfer functions are also utilized to assign different weights to the decomposed components so that users can emphasize features of specific interest in the volumes. Several examples of volume datasets together with their optimal positions of viewpoints are exhibited in order to demonstrate that the method can effectively guide naive users to find optimal projections of volumes.

Topological volume skeletons represent level-set graphs of 3D scalar fields, and have recently become crucial to visualizing the global isosurface transitions in the volume. However, it is still a time-consuming task to extract them, especially when input volumes are large-scale data and/or prone to small-amplitude noise. The paper presents an efficient method for accelerating the computation of such skeletons using adaptive tetrahedralization. The tetrahedralization is a top-down approach to linear interpolation of the scalar fields in that it selects tetrahedra to be subdivided adaptively using several criteria. As the criteria, the method employs a topological criterion as well as a geometric one in order to pursue all the topological isosurface transitions that may contribute to the global skeleton of the volume. The tetrahedralization also allows us to avoid unnecessary tracking of minor degenerate features that hide the global skeleton. Experimental results are included to demonstrate that the present method smoothes out the original scalar fields effectively without missing any significant topological features.

Direct volume rendering is a standard technique for projecting all the optically-encoded samples onto the screen at once to allow us to peer into the inner structures involved in a volume data. Datacentric approaches to the design of transfer functions (TFs) have recently been well-established, which perform mathematical analysis of the data prior to pertinent rendering. The advent of multidimensional TFs is one of the latest major achievements in the volume visualization research. As opposed to the traditional onedimensional TFs that only consider a voxel’s scalar field value, the multi-dimensional TFs assign auxiliary attributes to the voxels to construct their sophisticated parametric domains. For example, when visualizing volumes obtained by scientific simulations, the observers can utilize their own knowledge about the simulation settings to extract the global characteristics of the volumes and to locate regions of particular interest. If they are allowed to design multi-dimensional TFs using staff attributes so as to encapsulate such advance knowledge, they can readily yield visualization results to fulfill their purposes. Nevertheless, nearly all attributes for the conventional multi-dimensional TFs are based on local features, such as differentials and curvatures, and are difficult to capture the global structure of the volume contrary to the observer’s purposes. This paper therefore introduces a new set of topological attributes to establish a new framework that is intended to realize objective-based assistance. Topological attributes proposed herein are derived from the level-set graph, which delineates the topological evolution of an isosurface with respect to the scalar field.

It is still challenging to generate hand-drawn pictures because they differ from ordinary photographs in that they are often drawn as seen from multiple viewpoints. This paper presents a new approach for modeling such surperspective projection based on shape deformation techniques. Specifically, surperspective landscape images for guide-maps are generated from 3D geographical elevation data. Our method first partitions a target geographical surface into feature areas to provide designers with landmarks suitable for editing. The system takes as input 2D visual effects, which are converted to 3D geometric constraints for geographical surface deformation. Using ordinary perspective projection, the deformed shape is then transformed into a target guide-map image where each landmark enjoys its own vista points. An algorithm for calculating such 2D visual effects semi-automatically from the geographical shape features is also considered.

Abstract An initial overview of parallel visualization in the GeoFEM software system is provided. Our visualization subsystem offers many kinds of parallel visualization methods for the users to visualize their huge finite‐element analysis datasets for scalar, vector and/or tensor fields at a reasonable cost. A polygonal simplification scheme is developed to make the transmission and rendition of output graphic primitives more efficient. A salient feature of the subsystem lies in its capability in the automatic setting of visualization parameter values based on the analysis of scalar/flow field topology and volumetric coherence, to improve the quality of visualization results with a minimized number of batch re‐executions. Representative experimental results illustrate the effectiveness of our subsystem. Copyright © 2002 John Wiley & Sons, Ltd.

This paper describes initial results of a 3D field topology analysis for automating transfer function design aiming at comprehensible volume rendering. The conventional Reeb graph-based approach to describing topological features of 3D surfaces is extended to capture the topological skeleton of a volumetric field. Based on the analysis result, which is represented in the form of a hyper Reeb graph, a procedure is proposed for designing appropriate color/opacity transfer functions. Two analytic volume datasets are used to preliminarily prove the feasibility of the present design methodology.

Modular visualization environments (MVEs) have recently been regarded as the de facto standard for scientific data visualization, mainly due to adoption of the visual programming style, reusability, and extendability. However, since scientists and engineers as the MVE principal user are not always familiar with how to map numerical data to proper graphical primitives, the set of built-in modules is not fully used to construct necessary application networks. Therefore, a certain mechanism needs to be incorporated into MVEs, which makes use of heuristics and expertise of visualization specialists (visineers), and which supports the user in designing his/her applications with MVEs. The Wehrend's goal-oriented taxonomy of visualization techniques is adopted as the basic philosophy to develop a system, called GADGET, for application design guidance for MVEs. The GADGET system interactively helps the user design appropriate applications according to the specific visualization goals, temporal efficiency versus accuracy requirements, and such properties as dimension and mesh type of a given target dataset. Also the GADGET system is capable of assisting the user in customizing a prototype modular network for his/her desired applications by showing execution examples involving datasets of the same type. The paper provides an overview of the GADGET guidance mechanism and system architecture, with an emphasis on its knowledge base design. Sample data visualization problems are used to demonstrate the usefulness of the GADGET system.

A new type of geometric model called Interval volume for volumetric data exploration is presented. An interval volume represents a three dimensional subvolume for which the associate scalar values lie within a user specified interval, and provides one of the promising approaches to solid fitting, which is an extended concept of traditional surface fitting. A well known isosurfacing algorithm called Marching Cubes is extended to obtain a solid fitting algorithm, which extracts from a given volumetric data set a high resolution, polyhedral solid data structure of an interval volume. Branch-on-Need Octree is used as an auxiliary data structure to accelerate the extraction process. A variety of interval volume rendering methods and principal related operations, including measurements and focusing, are also presented. The effectiveness of measurement coupled visualization capabilities of the presented approach is demonstrated by application to visualizing a four dimensional simulated data from atomic collision research.