In this paper, we propose a novel polygonal remeshing technique that exploits a key aspect of surfaces: the intrinsic anisotropy of natural or man-made geometry. In particular, we use curvature directions to drive the remeshing process, mimicking the lines that artists themselves would use when creating 3D models from scratch. After extracting and smoothing the curvature tensor field of an input geometry patch, lines of minimum and maximum curvatures are used to determine appropriate edges for the remeshed version in anisotropic regions, while spherical regions are simply point-sampled since there is no natural direction of symmetry locally. As a result our technique generates polygon meshes mainly composed of quads in anisotropic regions, and of triangles in spherical regions. Our approach provides the flexibility to produce meshes ranging from isotropic to anisotropic, from coarse to dense, and from uniform to curvature adapted.

We present hardware-accelerated texture advection techniques to visualize the motion of particles in steady or time-varying vector fields given on Cartesian grids. We propose an implementation of 2D texture advection which exploits advanced and programmable texture fetch and per-pixel blending operations on an nVidia GeForce 3. For 3D vector field visualization, we present an algorithm for SGI's VPro, based on pixel textures and 3D textures. Moreover, we sketch how 3D texture advection could be implemented on future graphics boards that provide programmable fetch operations for 3D textures. Since all implementations exclusively use graphics hardware without intermediate data transfer to main memory, extremely high frame rates are achieved, e.g., up to 90 frames per second for advecting a calculatory number of one million particles in a 2D flow. The proposed techniques are especially useful for the interactive visualization of vector fields. 1

We investigate two important, common fluid flow patterns from computational fluid dynamics (CFD) simulations, namely, swirl and tumble motion typical of automotive engines. We study and visualize swirl and tumble flow using three different flow visualization techniques: direct, geometric, and texture-based. When illustrating these methods side-by-side, we describe the relative strengths and weaknesses of each approach within a specific spatial dimension and across multiple spatial dimensions typical of an engineer 's analysis. Our study is focused on steady-state flow. Based on this investigation we offer perspectives on where and when these techniques are best applied in order to visualize the behavior of swirl and tumble motion.

Abstract—Most fingerprint-based biometric systems store the minutiae template of a user in the database. It has been traditionally assumed that the minutiae template of a user does not reveal any information about the original fingerprint. In this paper, we challenge this notion and show that three levels of information about the parent fingerprint can be elicited from the minutiae template alone, viz., 1) the orientation field information, 2) the class or type information, and 3) the friction ridge structure. The orientation estimation algorithm determines the direction of local ridges using the evidence of minutiae triplets. The estimated orientation field, along with the given minutiae distribution, is then used to predict the class of the fingerprint. Finally, the ridge structure of the parent fingerprint is generated using streamlines that are based on the estimated orientation field. Line Integral Convolution is used to impart texture to the ensuing ridges, resulting in a ridge map resembling the parent fingerprint. The salient feature of this noniterative method to generate ridges is its ability to preserve the minutiae at specified locations in the reconstructed ridge map. Experiments using a commercial fingerprint matcher suggest that the reconstructed ridge structure bears close resemblance to the parent fingerprint.

A hardware-based approach for visualizing unsteady flow fields by means of Lagrangian-Eulerian advection is presented. Both noise-based and dyebased texture advection is supported. The implementation allows the advection of each texture to be performed completely on the graphics hardware in a single-pass rendering. We discuss experiences with the interactive visualization of unsteady flow fields that become possible due to the high visualization speed of the hardware-based approach. 1

Abstract—Design and control of vector fields is critical for many visualization and graphics tasks such as vector field visualization, fluid simulation, and texture synthesis. The fundamental qualitative structures associated with vector fields are fixed points, periodic orbits, and separatrices. In this paper, we provide a new technique that allows for the systematic creation and cancellation of fixed points and periodic orbits. This technique enables vector field design and editing on the plane and surfaces with desired qualitative properties. The technique is based on Conley theory, which provides a unified framework that supports the cancellation of fixed points and periodic orbits. We also introduce a novel periodic orbit extraction and visualization algorithm that detects, for the first time, periodic orbits on surfaces. Furthermore, we describe the application of our periodic orbit detection and vector field simplification algorithms to engine simulation data demonstrating the utility of the approach. We apply our design system to vector field visualization by creating data sets containing periodic orbits. This helps us understand the effectiveness of existing visualization techniques. Finally, we propose a new streamline-based technique that allows vector field topology to be easily identified. Index Terms—Vector field design, vector field visualization, vector field topology, vector field simplification, Morse decomposition, Conley index, periodic orbit detection, connection graphs. 1

Flow visualization has been a very attractive field within visualization research for a long time already. Usually huge datasets need to be processed, which often consist of multi-variate data with a really large number of sample locations, often arranged in multiple time-steps. Recently, the ever increasing performance of computers again has become a driving factor for a new boom in flow visualization (FlowViz), especially in FlowViz based on additional computation such as feature extraction, vector field clustering, and topology extraction. In this state-of-the-art report, an attempt was made to (1) provide a useful categorization of FlowViz solutions, (2) give a survey-like overview about existing solutions, and (3) focus on recent work, especially in the field of FlowViz based on derived data. We give careful consideration as to how these topics are best organized for such a presentation. In separate sections we describe (a) direct FlowViz techniques such as using arrows, (b) FlowViz using integral object such as stream lines, (c) space-filling FlowViz, including, spot noise or line integral convolution, and (d) FlowViz based on derived data such as flow topology. Within those sections, the discussion of FlowViz literature is sub-structured accoring to the dimensionality of the flow data (from 2D to 3D).

Abstract—Seeding streamlines in 3D flow fields without considering their projections in screen space can produce visually cluttered rendering results. Streamlines will overlap or intersect with each other in the output image, which makes it difficult for the user to perceive the underlying flow structure. This paper presents a method to control the seeding and generation of streamlines in image space to avoid visual cluttering and allow a more flexible exploration of flow fields. In our algorithm, 2D images with depth maps generated by a variety of visualization techniques can be used as input from which seeds are placed and streamlines are generated. The density and rendering styles of streamlines can be flexibly controlled based on various criteria to improve visual clarity. With our image space approach, it is straightforward to implement the level of detail rendering, depth peeling, and stylized rendering of streamlines to allow for more effective visualization of 3D flow fields. Index Terms—Three-dimensional flow visualization, streamlines, streamline seeding, level of detail, depth peeling, stylized rendering. 1

An optimal combustion process within an engine block is central to the performance of many motorized vehicles. Associated with this process are two important patterns of flow: swirl and tumble motion, which optimize the mixing of fluid within each of an engine’s cylinders. Good visualizations are necessary to analyze these in-cylinder flows. The simulation data associated with in-cylinder tumble motion within a gas engine, given on an unstructured, time-varying and adaptive resolution CFD grid, demands robust visualization methods that apply to unsteady flow. We present a range of methods including integral, feature-based, and image-based schemes with the goal of extracting and visualizing these two important patterns of motion. We place a strong emphasis on automatic and semi-automatic methods, including topological analysis, that require little or no user input. We make effective use of animation to visualize the time-dependent simulation data and describe the challenges and some of the implementation measures necessary in an application of the presented methods to unstructured, time-varying and volumetric grids.

Flow visualization is a fascinating sub-branch of scientific visualization. With ever increasing computing power, it is possible to process ever more complex fluid simulations. However, a gap between data set sizes and our ability to visualize them remains. This is especially true for the field of flow visualization which deals with large, timedependent, multivariate simulation datasets. In this paper, geometry based flow visualization techniques form the focus of discussion. Geometric flow visualization methods place discrete objects in the vector field whose characteristics reflect the underlying properties of the flow. A great amount of progress has been made in this field over the last two decades. However, a number of challenges remain, including placement, speed of computation, and perception. In this survey, we review and classify geometric flow visualization literature according to the most important challenges when considering such a visualization, a central theme being the seeding object upon which they are based. This paper details our investigation into these techniques with discussions on their applicability and their relative merits and drawbacks. The result is an up-to-date overview of the current state-of-the-art that highlights both solved and unsolved problems in this rapidly evolving branch of research. It also serves as a concise introduction to the field of flow visualization research.