Paul Rosenthal

Surface Extraction from Multi-field Particle Volume Data Using Multi-dimensional Cluster Visualization

Data sets resulting from physical simulations typically contain a multitude of physical variables. It is, therefore, desirable that visualization methods take into account the entire multi-field volume data rather than concentrating on one variable. We present a visualization approach based on surface extraction from multi-field particle volume data. The surfaces segment the data with respect to the underlying multi-variate function. Decisions on segmentation properties are based on the analysis of the multi-dimensional feature space. The feature space exploration is performed by an automated multi-dimensional hierarchical clustering method, whose resulting density clusters are shown in the form of density level sets in a 3D star coordinate layout. In the star coordinate layout, the user can select clusters of interest. A selected cluster in feature space corresponds to a segmenting surface in object space. Based on the segmentation property induced by the cluster membership, we extract a surface from the volume data. Our driving applications are smoothed particle hydrodynamics (SPH) simulations, where each particle carries multiple properties. The data sets are given in the form of unstructured point-based volume data. We directly extract our surfaces from such data without prior resampling or grid generation. The surface extraction computes individual points on the surface, which is supported by an efficient neighborhood computation. The extracted surface points are rendered using point-based rendering operations. Our approach combines methods in scientific visualization for object-space operations with methods in information visualization for feature-space operations.

Direct isosurface extraction from scattered volume data

Isosurface extraction is a standard visualization method for scalar volume data and has been subject to research for decades. Nevertheless, to our knowledge, no isosurface extraction method exists that directly extracts surfaces from scattered volume data without 3D mesh generation or reconstruction over a structured grid. We propose a method based on spatial domain partitioning using a kd-tree and an indexing scheme for efficient neighbor search. Our approach consists of a geometry extraction and a rendering step. The geometry extraction step computes points on the isosurface by linearly interpolating between neighboring pairs of samples. The neighbor information is retrieved by partitioning the 3D domain into cells using a kd-tree. The cells are merely described by their index and bitwise index operations allow for a fast determination of potential neighbors. We use an angle criterion to select appropriate neighbors from the small set of candidates. The output of the geometry step is a point cloud representation of the isosurface. The final rendering step uses point-based rendering techniques to visualize the point cloud. Our direct isosurface extraction algorithm for scattered volume data produces results of quality close to the results from standard isosurface extraction algorithms for gridded volume data (like marching cubes). In comparison to 3D mesh generation algorithms (like Delaunay tetrahedrization), our algorithm is about one order of magnitude faster for the examples used in this paper.

Smooth Surface Extraction from Unstructured Point-based Volume Data Using PDEs

Smooth surface extraction using partial differential equations (PDEs) is a well-known and widely used technique for visualizing volume data. Existing approaches operate on gridded data and mainly on regular structured grids. When considering unstructured point-based volume data where sample points do not form regular patterns nor are they connected in any form, one would typically resample the data over a grid prior to applying the known PDE-based methods. We propose an approach that directly extracts smooth surfaces from unstructured point-based volume data without prior resampling or mesh generation. When operating on unstructured data one needs to quickly derive neighborhood information. The respective information is retrieved by partitioning the 3D domain into cells using a fed-tree and operating on its cells. We exploit neighborhood information to estimate gradients and mean curvature at every sample point using a four-dimensional least-squares fitting approach. Gradients and mean curvature are required for applying the chosen PDE-based method that combines hyperbolic advection to an isovalue of a given scalar field and mean curvature flow. Since we are using an explicit time-integration scheme, time steps and neighbor locations are bounded to ensure convergence of the process. To avoid small global time steps, one can use asynchronous local integration. We extract a smooth surface by successively fitting a smooth auxiliary function to the data set. This auxiliary function is initialized as a signed distance function. For each sample and for every time step we compute the respective gradient, the mean curvature, and a stable time step. With these informations the auxiliary function is manipulated using an explicit Euler time integration. The process successively continues with the next sample point in time. If the norm of the auxiliary function gradient in a sample exceeds a given threshold at some time, the auxiliary function is reinitialized to a signed distance function. After convergence of the evolvution, the resulting smooth surface is obtained by extracting the zero isosurface from the auxiliary function using direct isosurface extraction from unstructured point-based volume data and rendering the extracted surface using point-based rendering methods.

A framework for exploring multidimensional data with 3D projections

Visualization of high‐dimensional data requires a mapping to a visual space. Whenever the goal is to preserve similarity relations a frequent strategy is to use 2D projections, which afford intuitive interactive exploration, e.g., by users locating and selecting groups and gradually drilling down to individual objects. In this paper, we propose a framework for projecting high‐dimensional data to 3D visual spaces, based on a generalization of the Least‐Square Projection (LSP). We compare projections to 2D and 3D visual spaces both quantitatively and through a user study considering certain exploration tasks. The quantitative analysis confirms that 3D projections outperform 2D projections in terms of precision. The user study indicates that certain tasks can be more reliably and confidently answered with 3D projections. Nonetheless, as 3D projections are displayed on 2D screens, interaction is more difficult. Therefore, we incorporate suitable interaction functionalities into a framework that supports 3D transformations, predefined optimal 2D views, coordinated 2D and 3D views, and hierarchical 3D cluster definition and exploration. For visually encoding data clusters in a 3D setup, we employ color coding of projected data points as well as four types of surface renderings. A second user study evaluates the suitability of these visual encodings. Several examples illustrate the frameworks applicability for both visual exploration of multidimensional abstract (non‐spatial) data as well as the feature space of multi‐variate spatial data.

Linking Multidimensional Feature Space Cluster Visualization to Multifield Surface Extraction

Data sets resulting from physical simulations typically contain a multitude of physical variables. So, visualization methods should take into account the entire multifield volume data rather than concentrate on one variable. We have developed a visualization approach based on surface extraction from multifield volume data. The extracted surfaces segment the data with respect to an underlying multivariate function. Decisions on segmentation properties are based on the analysis of a multidimensional feature space. We perform feature space exploration using automated multidimensional hierarchical clustering. The hierarchical clusters appear as a cluster tree in a 2D radial layout. In this layout, the user can select clusters of interest. A selected cluster in feature space corresponds to a segmenting surface in object space. On the basis of the segmentation property induced by the cluster membership, we extract surfaces from the volume data.

Diverse Ecologies – Interdisciplinary Development for Cultural Education

We present a case study outlining development efforts towards an interface ecology to be deployed in museums. We argue that the problem at hand calls for a highly interdisciplinary design process. Furthermore, system design in the domain of cultural education poses a unique set of challenges. At the same time few existing design methodologies are suitable for addressing this special environment of system design. We outline a set of tentative methodological elements aimed at informing adequate interdisciplinary development processes. The discussion is embedded into a critique of existing methodologies while being orientated towards inviting critique itself. The guiding insight steering our methodological developments is that fundamental differences between project participants and other stakeholders should be construed as assets. Rather than trying to integrate them or covering them up, the dynamic friction between differing viewpoints can be rendered productive by means of poietic practices.

Enclosing surfaces for point clusters using 3d discrete voronoi diagrams

Point clusters occur in both spatial and non‐spatial data. In the former context they may represent segmented particle data, in the latter context they may represent clusters in scatterplots. In order to visualize such point clusters, enclosing surfaces lead to much better comprehension than pure point renderings.

VisRuption: intuitive and efficient visualization of temporal airline disruption data

The operation of an airline is a very complex task and disruptions to the planned operation can occur on very short notice. Already a small disruption like a delay of some minutes can cost the airline a tremendous amount of money. Hence, it is crucial to proactively control all operations of the airline and efficiently prioritize and handle disruptions. Due to the complex setting and the need for ad hoc decisions this task can only be carried out by human operation controllers. In the field of airline operations control there exists already a vast variety of different software in productive use. We analyze the different approaches from two of the market leaders and identify problematic design choices. We take into account this analysis and develop a set of rules for an intuitive visualization of airline disruption data. Finally, we introduce our tool for visualizing such data which complies to these rules. The visualization enables the user to gain a fast overview over the current problem situation and to intuitively prioritize different problems and problem hierarchies. The efficiency of the design is evaluated with the help of a user study which shows that the new system significantly outperforms the current state of the art.

Non-iterative second-order approximation of signed distance functions for any isosurface representation

Signed distance functions (SDF) to explicit or implicit surface representations are intensively used in various computer graphics and visualization algorithms. Among others, they are applied to optimize collision detection, are used to reconstruct data fields or surfaces, and, in particular, are an obligatory ingredient for most level set methods. Level set methods are common in scientific visualization to extract surfaces from scalar or vector fields. Usual approaches for the construction of an SDF to a surface are either based on iterative solutions of a special partial differential equation or on marching algorithms involving a polygonization of the surface. We propose a novel method for a non‐iterative approximation of an SDF and its derivatives in a vicinity of a manifold. We use a second‐order algebraic fitting scheme to ensure high accuracy of the approximation. The manifold is defined (explicitly or implicitly) as an isosurface of a given volumetric scalar field. The field may be given at a set of irregular and unstructured samples. Stability and reliability of the SDF generation is achieved by a proper scaling of weights for the Moving Least Squares approximation, accurate choice of neighbors, and appropriate handling of degenerate cases. We obtain the solution in an explicit form, such that no iterative solving is necessary, which makes our approach fast.

SmoothViz: Visualization of Smoothed Particles Hydrodynamics Data

Smoothed particle hydrodynamics (SPH) is a completely mesh-free method to simulate fluid flow (Gingold & Monaghan, 1977; Lucy, 1977). Rather than representing the physical variables on a fixed grid, the fluid is represented by freely moving interpolation centers (“particles”). Apart from their position and velocity these particles carry information about the physical quantities of the considered fluid, such as temperature, composition, chemical potentials, etc. As the method is completely Lagrangian and particles follow the motion of the flow, the particles represent an unstructured data set at each point in time, i.e., the particles do not exhibit a regular spatial arrangement nor a fixed connectivity. For a recent detailed review of modern formulations of the SPH method see Rosswog (2009). For the analysis of the simulation results, data visualization plays an important role. However, visualization methods need to account for the highly adaptive, unstructured data representation in SPH simulations. Reconstructing the entire data field over a regular grid is not an option, as it would either use grids of immense sizes that cannot be handled efficiently anymore or it inevitably would introduce significant interpolation errors. Such errors should be avoided, especially as they would occur most prominently in areas of high particle density, i.e., areas of highest importance are undersampled. Adaptive grids may be an option as interpolation errors can be kept low, but the adaptivity requires special treatments during the visualization process. In this chapter, we introduce visualization methods that operate directly on the particle data, i.e., on unstructured point-based volumetric data. Section 3 introduces an approach to directly extract isosurfaces from a scalar field of the SPH simulation. Isosurfaces extraction is a common visualization concept and is suitable for SPH data visualization, as one is often interested in seeing boundaries of certain features. Because of the use of radial kernel functions in SPH computations (which is crucial for exact conservation of energy, momentum, and angular momentum) together with a poor a resolution, one can observe that the extracted isosurfaces may be bumpy, especially in regions of low particle density. We approach this issue by introducing level-set methods for 1