Figure 1: Our approach seamlessly embeds wheelchair challenges into immersive VR horror game elements to raise accessibility awareness in a playful, non-didactic manner.The left image showcases the virtual wheelchair and its per-wheel physics simulations, including phenomena such as wheel slips on wet surfaces.The other screenshots depict our testbed game that incorporates seven basic wheelchair challenges and is set in a hotel building infiltrated by a monster.

In this paper, we analyze the Apple Vision Pro hardware and the visionOS software platform, assessing their capabilities for volume rendering of structured grids—a prevalent technique across various applications. The Apple Vision Pro supports multiple display modes, from classical augmented reality (AR) using video see-through technology to immersive virtual reality (VR) environments that exclusively render virtual objects. These modes utilize different APIs and exhibit distinct capabilities. Our focus is on direct volume rendering, selected for its implementation challenges due to the native graphics APIs being predominantly oriented towards surface shading. Volume rendering is particularly vital in fields where AR and VR visualizations offer substantial benefits, such as in medicine and manufacturing. Despite its initial high cost, we anticipate that the Vision Pro will become more accessible and affordable over time, following Apple’s track record of market expansion. As these devices become more prevalent, understanding how to effectively program and utilize them becomes increasingly important, offering significant opportunities for innovation and practical applications in various sectors.

Digital games allow us to adopt the avatar's perspective and experience the (virtual) world from another point of view. The positive effects of such role swaps are well-documented in contexts such as combating racial or ethnic biases. Our work expands this research direction by considering animal avatars: We hypothesize that playing as an animal can improve our attitude towards wildlife and nature. In particular, this manuscript contributes a first systematic study on the effects of animal avatars on players' implicit attitudes towards animals. Our study (N = 78) employs two commercial games featuring different animal avatars, Endling - Extinction is Forever and Bee Simulator, and compares the results of implicit association tests administered before and after gameplay. Our findings confirm significant positive effects of the games on players' implicit associations with foxes and bees. Together with several interesting insights regarding player-avatar relationships and avatar identification, our work paves the groundwork for games contributing positively to societal change.

Virtual Reality (VR) exergames can increase engagement in and motivation for physical activities. Most VR exergames focus on the upper body because many VR setups only track the users’ heads and hands. To become a serious alternative to existing exercise programs, VR exergames must provide a balanced workout and train the lower limbs, too. To address this issue, we built a VR exergame focused on vertical jump training to explore full-body exercise applications. To create a safe and effective training, nine domain experts participated in our prototype design. Our mixed-methods study confirms that the jump-centered exercises provided a worthy challenge and positive player experience, indicating long-term retention. Based on our findings, we present five design implications to guide future work: avoid an unintended forward drift, consider technical constraints, address safety concerns in full-body VR exergames, incorporate rhythmic elements with fluent movement patterns, adapt difficulty to players’ fitness progression status.

In virtual reality games, players experience virtual worlds that often exceed the size of the actual tracking area. Small, stationary setups are especially problematic as they fail the requirements of most locomotion techniques. Although gesture-based approaches, such as walking-in-place, work in minimal playspaces, they often lead to unintentional movements or poor precision. Hence, developers often revert to less realistic virtual navigation concepts. We present a novel locomotion technique for VR games addressing this problem. Depending on the desired movement, players dynamically switch between two modes: point-tugging and arm-swinging. Tugging the controllers at chest height is used for precise maneuvering; swinging them at waist level triggers an accelerated forward motion for long travels. A within- participant study reveals that our concept avoids cybersickness while enhancing physical activity and presence.

Virtual reality (VR) games intensify storytelling experiences by letting players take the role of a character. However, in contrast to films, novels, or games, VR experiences often remain centered around one single character without using the potential of complex multiprotagonist plots. Our work engages in this critical topic by investigating the design of immersive and natural transitions between different characters. First, we conducted a scoping review to identify existing multiprotagonist VR games (N=18) and grouped their used transition techniques into four categories. Based on these findings and prior research, we designed two transition techniques (Static Map vs. Rebodying) and conducted a between-participants (N=36) study to explore their effect on user experience. Our results show that Rebodying outperforms Static Map regarding the perceived realism, acceptance, and spatial understanding of the character transitions. Both conditions do not differ significantly in terms of cybersickness. Finally, we provide future directions for developing, improving, and exploring of multiprotagonist transition techniques in VR games.

We frequently utilize face emojis to express emotions in digital communication. But how wholly and precisely do such pictographs sample the emotional spectrum, and are there gaps to be closed? Our research establishes emoji intensity scales for seven basic emotions: happiness, anger, disgust, sadness, shock, annoyance, and love. In our survey (N = 1195), participants worldwide assigned emotions and intensities to 68 face emojis. According to our results, certain feelings, such as happiness or shock, are visualized by manifold emojis covering a broad spectrum of intensities. Other feelings, such as anger, have limited and only very intense representative visualizations. We further emphasize that the cultural background influences emojis’ perception: for instance, linear-active cultures (e.g., UK, Germany) rate the intensity of such visualizations higher than multi-active (e.g., Brazil, Russia) or reactive cultures (e.g., Indonesia, Singapore). To summarize, our manuscript promotes future research on more expressive, culture-aware emoji design.

The size of most virtual environments exceeds the tracking space available for physical walking. One solution to this disparity is to extend the available walking range by augmenting users' actual movements. However, the resulting increase in visual flow can easily cause cybersickness. Therefore, we present a novel augmented-walking approach for virtual reality games. Our core concept is a virtual tunnel that spans the entire travel distance when viewed from the outside. However, its interior is only a fraction as long, allowing users to cover the distance by real walking. Whereas the tunnel hides the visual flow from the applied movement acceleration, windows on the tunnel's walls still reveal the actual expedited motion. Our evaluation reveals that our approach avoids cybersickness while enhancing physical activity and preserving presence. We finish our paper with a discussion of the design considerations and limitations of our proposed locomotion technique.

The spectatorship experience for virtual reality (VR) games differs strongly from its non-VR precursor. When watching non-VR games on platforms such as Twitch, spectators just see what the player sees, as the physical interaction is mostly unimportant for the overall impression. In VR, the immersive full-body interaction is a crucial part of the player experience. Hence, content creators, such as streamers, often rely on green screens or similar solutions to offer a mixed-reality third-person view to disclose their full-body actions. Our work compares the most popular realizations of the first-person and the third-person perspective in an online survey (N = 217) with three different VR games. Contrary to the current trend to stream in third-person, our key result is that most viewers prefer the first-person version, which they attribute mostly to the better focus on in-game actions and higher involvement. Based on the study insights, we provide design recommendations for both perspectives.

Driven by the games community, virtual reality setups have lately evolved into affordable and consumer-ready mobile headsets. However, despite these promising improvements, it remains challenging to convey immersive and engaging VR games as players are usually limited to experience the virtual world by vision and hearing only. One prominent example of such open challenges is the disparity between the real surroundings and the virtual environment. As virtual obstacles usually do not have a physical counterpart, players might walk through walls enclosing the level. Thus, past research mainly focussed on multisensory collision feedback to deter players from ignoring obstacles. However, the underlying causative reasons for such unwanted behavior have mostly remained unclear. Our work investigates how task types and wall appearances influence the players' incentives to walk through virtual walls. Therefore, we conducted a user study, confronting the participants with different task motivations and walls of varying opacity and realism. Our evaluation reveals that players generally adhere to realistic behavior, as long as the experience feels interesting and diverse. Furthermore, we found that opaque walls excel in deterring subjects from cutting short, whereas different degrees of realism had no significant influence on walking trajectories. Finally, we use collected player feedback to discuss individual reasons for the observed behavior.

On a journey, a backpack is a perfect place to store and organize the necessary provisions and tools. Similarly, carrying and managing items is a central part of most digital games, providing significant prospects for the player experience. Even though VR games are gradually becoming more mature, most of them still avoid this essential feature. Some of the reasons for this deficit are the additional requirements and challenges that VR imposes on developers to achieve a compelling user experience. We structure the ample design space of VR inventories by analyzing popular VR games and developing a structural taxonomy. We combine our insights with feedback from game developers to identify the essential building blocks and design choices. Finally, we propose meaningful design implications and demonstrate the practical use of our work in action.

Using virtual reality setups, users can fade out of their surroundings and dive fully into a thrilling and appealing virtual environment. The success of such immersive experiences depends heavily on natural and engaging interactions with the virtual world. As developers tend to focus on intuitive hand controls, other aspects of the broad range of full-body capabilities are easily left vacant. One repeatedly overlooked input modality is the user's gait. Even though users may walk physically to explore the environment, it usually does not matter how they move. However, gait-based interactions, using the variety of information contained in human gait, could offer interesting benefits for immersive experiences. For instance, stealth VR-games could profit from this additional range of interaction fidelity in the form of a sneaking-based input modality. In our work, we explore the potential of sneaking as a playful input modality for virtual environments. Therefore, we discuss possible sneaking-based gameplay mechanisms and develop three technical approaches, including precise foot-tracking and two abstraction levels. Our evaluation reveals the potential of sneaking-based inter-actions in IVEs, offering unique challenges and thrilling gameplay. For these interactions, precise tracking of individual footsteps is unnecessary, as a more abstract approach focusing on the players' intention offers the same experience while providing better comprehensible feedback. Based on these findings, we discuss the broader potential and individual strengths of our gait-centered interactions.

Escape rooms exist in various forms, including real-life facilities, board games, and digital implementations. The underlying idea is always the same: players have to solve many diverse puzzles to (virtually) escape from a locked room. Within the last decade, we witnessed a rapidly increasing popularity of such games, which also amplified the amount of related research. However, the respective academic landscape is mostly fragmented in its current state, lacking a common model and vocabulary that would withstand these games' variety. This manuscript aims to establish such a foundation for the analysis and construction of escape rooms. In a first step, we derive a high-level design framework from prior literature. Then, as our main contribution, we establish an atomic puzzle taxonomy that closes the gap between the analog and digital domains. The taxonomy is developed in multiple steps: we compose a basic structure based on previous literature and systematically refine it by analyzing 39 analog and digital escape room games, including recent virtual reality representatives. The final taxonomy consists of mental, physical, and emotional challenges, thereby providing a robust and approachable basis for future works across all application domains that deal with escape rooms or puzzles in general.

Dense flow visualization is a popular visualization paradigm. Traditionally, the various models and methods in this area use a continuous formulation, resting upon the solid foundation of functional analysis. In this work, we examine a discrete formulation of dense flow visualization. From probability theory, we derive a similarity matrix that measures the similarity between different points in the flow domain, leading to the discovery of a whole new class of visualization models. Using this matrix, we propose a novel visualization approach consisting of the computation of spectral embeddings, i.e., characteristic domain maps, defined by particle mixture probabilities. These embeddings are scalar fields that give insight into the mixing processes of the flow on different scales. The approach of spectral embeddings is already well studied in image segmentation, and we see that spectral embeddings are connected to Fourier expansions and frequencies. We showcase the utility of our method using different 2D and 3D flows.

Nowadays, many wearable devices such as smartwatches exist that can be used to track and analyze sports activities. Generally, these devices are equipped with high-resolution screens, but most applications provide only textual status information as real-time visual feedback during the respective activities. This limited amount of information is particularly the case for running, which is among the most frequently tracked sports activities with wearable devices. So far, only a few products and prototypes provide assistance and feedback related to running technique and efficiency, but also predominantly by means of textual data representations.

Visualizations rely on highlighting to attract and guide our attention. To make an object of interest stand out independently from a number of distractors, the underlying visual cue, e.g., color, has to be preattentive. In our prior work, we introduced Deadeye as an instantly recognizable highlighting technique that works by rendering the target object for one eye only. In contrast to prior approaches, Deadeye excels by not modifying any visual properties of the target. However, in the case of 2D visualizations, the method requires an additional setup to allow dichoptic presentation, which is a considerable drawback. As a follow-up to requests from the community, this paper explores Deadeye as a highlighting technique for 3D visualizations, because such stereoscopic scenarios support dichoptic presentation out of the box. Deadeye suppresses binocular disparities for the target object, so we cannot assume the applicability of our technique as a given fact. With this motivation, the paper presents quantitative evaluations of Deadeye in VR, including configurations with multiple heterogeneous distractors as an important robustness challenge. After confirming the preserved preattentiveness (all average accuracies above 90%) under such real-world conditions, we explore VR volume rendering as an example application scenario for Deadeye. We depict a possible workflow for integrating our technique, conduct an exploratory survey to demonstrate benefits and limitations, and finally provide related design implications.

Virtual reality offers the unique possibility to experience a virtual representation as our own body. In contrast to previous research that predominantly studied this phenomenon for humanoid avatars, our work focuses on virtual animals. In this paper, we discuss different body tracking approaches to control creatures such as spiders or bats and the respective virtual body ownership effects. Our empirical results demonstrate that virtual body ownership is also applicable for nonhumanoids and can even outperform human-like avatars in certain cases. An additional survey confirms the general interest of people in creating such experiences and allows us to initiate a broad discussion regarding the applicability of animal embodiment for educational and entertainment purposes.

In this paper, we present a novel approach to automated route generation of global positioning system (GPS) artwork. The term GPS artwork describes the generation of drawings by leaving virtual traces on digital maps. Until now, the creation of these images has required a manual planning phase in which an artist designs the route by hand. Once the route for this artwork has been planned, GPS devices have been used to track the movement. Using our solution, the lengthy planning phase can be significantly shortened, thereby opening art creation to a broader public.

Preattentive visual features such as hue or flickering can effectively draw attention to an object of interest - for instance, an important feature in a scientific visualization. These features appear to pop out and can be recognized by our visual system, independently from the number of distractors. Most cues do not take advantage of the fact that most humans have two eyes. In cases where binocular vision is applied, it is almost exclusively used to convey depth by exposing stereo pairs. We present Deadeye, a novel preattentive visualization technique based on presenting different stimuli to each eye. The target object is rendered for one eye only and is instantly detected by our visual system. In contrast to existing cues, Deadeye does not modify any visual properties of the target and, thus, is particularly suited for visualization applications. Our evaluation confirms that Deadeye is indeed perceived preattentively. We also explore a conjunction search based on our technique and show that, in contrast to 3D depth, the task cannot be processed in parallel.

Presenting content to an audience is important in several fields, including education, marketing, and entertainment. Therefore, the main goal of the presenter is to transport messages to the audience.

Abstract A central topic in scientific visualization is the transfer function (TF) for volume rendering. The TF serves a fundamental role in translating scalar and multivariate data into color and opacity to express and reveal the relevant features present in the data studied. Beyond this core functionality, TFs also serve as a tool for encoding and utilizing domain knowledge and as an expression for visual design of material appearances. TFs also enable interactive volumetric exploration of complex data. The purpose of this state‐of‐the‐art report (STAR) is to provide an overview of research into the various aspects of TFs, which lead to interpretation of the underlying data through the use of meaningful visual representations. The STAR classifies TF research into the following aspects: dimensionality, derived attributes, aggregated attributes, rendering aspects, automation, and user interfaces. The STAR concludes with some interesting research challenges that form the basis of an agenda for the development of next generation TF tools and methodologies.

This paper presents a new multi-resolution volume representation called sparse pdf volumes, which enables consistent multi-resolution volume rendering based on probability density functions (pdfs) of voxel neighborhoods. These pdfs are defined in the 4D domain jointly comprising the 3D volume and its 1D intensity range. Crucially, the computation of sparse pdf volumes exploits data coherence in 4D, resulting in a sparse representation with surprisingly low storage requirements. At run time, we dynamically apply transfer functions to the pdfs using simple and fast convolutions. Whereas standard low-pass filtering and down-sampling incur visible differences between resolution levels, the use of pdfs facilitates consistent results independent of the resolution level used. We describe the efficient out-of-core computation of large-scale sparse pdf volumes, using a novel iterative simplification procedure of a mixture of 4D Gaussians. Finally, our data structure is optimized to facilitate interactive multi-resolution volume rendering on GPUs.

As scientific data of increasing size is generated by today's simulations and measurements, utilizing dedicated server resources to process the visualization pipeline becomes necessary. In a purely server-based approach, requirements on the client-side are minimal as the client only displays results received from the server. However, the client may have a considerable amount of hardware available, which is left idle. Further, the visualization is put at the whim of possibly unreliable server and network conditions. Server load, bandwidth and latency may substantially affect the response time on the client. In this paper, we describe a hybrid method, where visualization workload is assigned to server and client. A capable client can produce images independently. The goal is to determine a workload schedule that enables a synergy between the two sides to provide rendering results to the user as fast as possible. The schedule is determined based on processing and transfer timings obtained at runtime. Our probabilistic scheduler adapts to changing conditions by shifting workload between server and client, and accounts for the performance variability in the dynamic system.

In recent years high-resolution tiled display systems have gained significant attention in scientific and information visualization of large-scale data. Modern tiled display setups are based on either video projectors or LCD screens. While LCD screens are the preferred solution for monoscopic setups, stereoscopic displays almost exclusively consist of some kind of video projection. This is because projections can significantly reduce gaps between tiles, while LCD screens require a bezel around the panel. Projection setups, however, suffer from a number of maintenance issues that are avoided by LCD screens. For example, projector alignment is a very time-consuming task that needs to be repeated at intervals, and different aging states of lamps and filters cause color inconsistencies. The growing availability of inexpensive stereoscopic LCDs for television and gaming allows one to build high-resolution stereoscopic tiled display walls with the same dimensions and resolution as projection systems at a fraction of the cost, while avoiding the aforementioned issues. The only drawback is the increased gap size between tiles.

In recent years, there has been significant growth in the use of patient-specific models to predict the effects of neuromodulation therapies such as deep brain stimulation (DBS). However, translating these models from a research environment to the everyday clinical workflow has been a challenge, primarily due to the complexity of the models and the expertise required in specialized visualization software. In this paper, we deploy the interactive visualization system ImageVis3D Mobile, which has been designed for mobile computing devices such as the iPhone or iPad, in an evaluation environment to visualize models of Parkinson's disease patients who received DBS therapy. Selection of DBS settings is a significant clinical challenge that requires repeated revisions to achieve optimal therapeutic response, and is often performed without any visual representation of the stimulation system in the patient. We used ImageVis3D Mobile to provide models to movement disorders clinicians and asked them to use the software to determine: 1) which of the four DBS electrode contacts they would select for therapy; and 2) what stimulation settings they would choose. We compared the stimulation protocol chosen from the software versus the stimulation protocol that was chosen via clinical practice (independent of the study). Lastly, we compared the amount of time required to reach these settings using the software versus the time required through standard practice. We found that the stimulation settings chosen using ImageVis3D Mobile were similar to those used in standard of care, but were selected in drastically less time. We show how our visualization system, available directly at the point of care on a device familiar to the clinician, can be used to guide clinical decision making for selection of DBS settings. In our view, the positive impact of the system could also translate to areas other than DBS.

In this work, we present an intuitive image-quality metric that is derived from the motivation of DVF visualization. It utilizes the features of the resulting image and effectively measures the similarity between the output of the visualization method and the input flow data. We use the angle between the gradient direction and the original vector field as a measure of such similarity and the gradient magnitude as an importance measure. Our metric enables the automatic evaluation of images for a given vector field and allows the comparison of different methods, parameters sets, and quality improvement strategies for a specific vector field. By integrating the metric into the image-computation process, our approach can be used to generate improved images by choosing the best parameter set. To verify the effectiveness of our method, we conducted an extensive user study that demonstrated the metric’s applicability to various situations. For instance, our approach elucidated the robustness of a DVF visualization in the presence of data-altering filters, such as resampling.

Dense flow visualization and streamlines are among the most popular methods in numerous applications of scientific visualization. Because very few works synthesize the benefits of these approaches into one framework, we propose a hybrid technique. In this work, we present a novel method of dense flow visualization that produces high contrast images (typical of streamline approaches), with manageable spatial frequency. We begin by introducing a notion of a streamline thickness function and thickness wave sources, and then compute the resulting image as a coherent wave interference pattern. We show that this problem is equivalent to finding the dominant eigenvector of a sparse matrix and employ the power iterations numerical scheme to demonstrate an efficient parallel implementation. We conclude by discussing possible quality improvement strategies and extensions of our method to other domains, including 3D flows.

We introduce a new type of multi-resolution image pyramid for high-resolution images called sparse pdf maps (sPDF-maps). Each pyramid level consists of a sparse encoding of continuous probability density functions (pdfs) of pixel neighborhoods in the original image. The encoded pdfs enable the accurate computation of non-linear image operations directly in any pyramid level with proper pre-filtering for anti-aliasing, without accessing higher or lower resolutions. The sparsity of sPDF-maps makes them feasible for gigapixel images, while enabling direct evaluation of a variety of non-linear operators from the same representation. We illustrate this versatility for antialiased color mapping, O ( n ) local Laplacian filters, smoothed local histogram filters (e.g., median or mode filters), and bilateral filters.

Image population analysis is the class of statistical methods that plays a central role in understanding the development, evolution, and disease of a population. However, these techniques often require excessive computational power and memory that are compounded with a large number of volumetric inputs. Restricted access to supercomputing power limits its influence in general research and practical applications. In this paper we introduce ISP, an Image-Set Processing streaming framework that harnesses the processing power of commodity heterogeneous CPU/GPU systems and attempts to solve this computational problem. In ISP, we introduce specially designed streaming algorithms and data structures that provide an optimal solution for out-of-core multiimage processing problems both in terms of memory usage and computational efficiency. ISP makes use of the asynchronous execution mechanism supported by parallel heterogeneous systems to efficiently hide the inherent latency of the processing pipeline of out-of-core approaches. Consequently, with computationally intensive problems, the ISP out-of-core solution can achieve the same performance as the in-core solution. We demonstrate the efficiency of the ISP framework on synthetic and real datasets.

In this paper, we present a sample-based approach for surface coloring, which is independent of the original surface resolution and representation. To achieve this, we introduce the Orthogonal Fragment Buffer (OFB)-an extension of the Layered Depth Cube-as a high-resolution view-independent surface representation. The OFB is a data structure that stores surface samples at a nearly uniform distribution over the surface, and it is specifically designed to support efficient random read/write access to these samples. The data access operations have a complexity that is logarithmic in the depth complexity of the surface. Thus, compared to data access operations in tree data structures like octrees, data-dependent memory access patterns are greatly reduced. Due to the particular sampling strategy that is employed to generate an OFB, it also maintains sample coherence, and thus, exhibits very good spatial access locality. Therefore, OFB-based surface coloring performs significantly faster than sample-based approaches using tree structures. In addition, since in an OFB, the surface samples are internally stored in uniform 2D grids, OFB-based surface coloring can efficiently be realized on the GPU to enable interactive coloring of high-resolution surfaces. On the OFB, we introduce novel algorithms for color painting using volumetric and surface-aligned brushes, and we present new approaches for particle-based color advection along surfaces in real time. Due to the intermediate surface representation we choose, our method can be used to color polygonal surfaces as well as any other type of surface that can be sampled.

Abstract Efficient sorting is a key requirement for many computer science algorithms. Acceleration of existing techniques as well as developing new sorting approaches is crucial for many real‐time graphics scenarios, database systems, and numerical simulations to name just a few. It is one of the most fundamental operations to organize and filter the ever growing massive amounts of data gathered on a daily basis. While optimal sorting models for serial execution on a single processor exist, efficient parallel sorting remains a challenge. In this paper, we present a hardware‐optimized parallel implementation of the radix sort algorithm that results in a significant speed up over existing sorting implementations. We outperform all known General Processing Unit (GPU) based sorting systems by about a factor of two and eliminate restrictions on the sorting key space. This makes our algorithm not only the fastest, but also the first general GPU sorting solution.

Particle tracing has been established as a powerful visualization technique to show the dynamics of 3D flows. Particle tracing in 3D, however, quickly overextends the viewer due to the massive amount of visual information that is typically produced by this technique. In this paper, we present strategies to reduce this amount at the same time revealing important structures in the flow. As an importance measure, we introduce a simple, yet effective clustering approach for vector fields, and we use scalar flow quantities at different scales in combination with user-defined regions of interest. These measures are used to control the shape, the appearance, and the density of particles in such a way that the user can focus on the dynamics in important regions at the same time preserving context information. We also introduce a new focus for particle tracing, so called anchor lines. Anchor lines are used to analyze local flow features by visualizing how much particles separate over time and how long it takes until they have separated to a fixed distance. It is of particular interest if the finite time Lyapunov exponent - a scalar quantity that measures the rate of separation of infinitesimally close particles in the flow - is used to guide the placement of anchor lines. The effectiveness of our approaches for the visualization of 3D flow fields is validated using synthetic fields as well as real simulation data.

In this work we present basic methodology for interactive volume editing on GPUs, and we demonstrate the use of these methods to achieve a number of different effects. We present fast techniques to modify the appearance and structure of volumetric scalar fields given on Cartesian grids. Similar to 2D circular brushes as used in surface painting we present 3D spherical brushes for intuitive coloring of particular structures in such fields. This paint metaphor is extended to allow the user to change the data itself, and the use of this functionality for interactive structure isolation, hole filling, and artefact removal is demonstrated. Building on previous work in the field we introduce high-resolution selection volumes, which can be seen as a resolution-based focus+context metaphor. By utilizing such volumes we present a novel approach to interactive volume editing at sub-voxel accuracy. Finally, we introduce a fast technique to paste textures onto iso-surfaces in a 3D scalar field. Since the texture resolution is independent of the volume resolution, this technique allows structure-aligned textures containing appearance properties or textual information to be used for volume augmentation and annotation.

In this paper we present GPU-based techniques for the interactive visualization of large unsteady 3D flow fields on uniform grids. We propose a novel dual-core approach to asynchronously stream such fields from the CPU, thus enabling the efficient exploration of large time-resolved sequences. This approach decouples visualization from data handling, resulting in interactive frame rates. Built upon a previously published GPU particle engine for flow visualization we have developed new strategies to compute and to visualize path lines and streak lines on the GPU. To provide additional visual cues, focus+context techniques for polygonal meshes have been integrated. The proposed techniques are used in the visual analysis of the Terashake 2.1 earthquake simulation data, and they have been shown to be very effective in revealing the relevant information in this data.

Volume rendered imagery often includes a barrage of 3D information like shape, appearance and topology of complex structures, and it thus quickly overwhelms the user. In particular, when focusing on a specific region a user cannot observe the relationship between various structures unless he has a mental picture of the entire data. In this paper we present ClearView, a GPU-based, interactive framework for texture-based volume ray-casting that allows users which do not have the visualization skills for this mental exercise to quickly obtain a picture of the data in a very intuitive and user-friendly way. ClearView is designed to enable the user to focus on particular areas in the data while preserving context information without visual clutter. ClearView does not require additional feature volumes as it derives any features in the data from image information only. A simple point-and-click interface enables the user to interactively highlight structures in the data. ClearView provides an easy to use interface to complex volumetric data as it only uses transparency in combination with a few specific shaders to convey focus and context information.

In this paper we introduce GPU particle tracing for the visualization of 3D diffusion tensor fields. For about half a million particles, reconstruction of diffusion directions from the tensor field, time integration and rendering can be done at interactive rates. Different visualization options like oriented particles of diffusion-dependent shape, stream lines or stream tubes facilitate the use of particle tracing for diffusion tensor visualization. The proposed methods provide efficient and intuitive means to show the dynamics in diffusion tensor fields, and they accommodate the exploration of the diffusion properties of biological tissue.

As simulation and rendering capabilities continue to increase, volumetric effects like smoke, fire or explosions will be frequently encountered in computer games and virtual environments. In this paper, we present techniques for the visual simulation and rendering of such effects that keep up with the demands for frame rates imposed by such environments. This is achieved by leveraging functionality on recent graphics programming units (GPUs) in combination with a novel approach to model non physics-based, yet realistic variations in flow fields. We show how to use this mechanism for simulating effects as demonstrated in Figure 1. Physics-based simulation is performed on 2D proxy geometries, and simulation results are extruded to 3D using particle or texture based approaches. Our method allows the animator to model and to flexibly control the dynamic behavior of volumetric effects, and it can be used to create plausible animations of a variety of natural phenomena.

We present a particle system for interactive visualization of steady 3D flow fields on uniform grids. For the amount of particles we target, particle integration needs to be accelerated and the transfer of these sets for rendering must be avoided. To fulfill these requirements, we exploit features of recent graphics accelerators to advect particles in the graphics processing unit (GPU), saving particle positions in graphics memory, and then sending these positions through the GPU again to obtain images in the frame buffer. This approach allows for interactive streaming and rendering of millions of particles and it enables virtual exploration of high resolution fields in a way similar to real-world experiments. The ability to display the dynamics of large particle sets using visualization options like shaded points or oriented texture splats provides an effective means for visual flow analysis that is far beyond existing solutions. For each particle, flow quantities like vorticity magnitude and wavelength2 are computed and displayed. Built upon a previously published GPU implementation of a sorting network, visibility sorting of transparent particles is implemented. To provide additional visual cues, the GPU constructs and displays visualization geometry like particle lines and stream ribbons.

Nowadays, direct volume rendering via 3D textures has positioned itself as an efficient tool for the display and visual analysis of volumetric scalar fields. It is commonly accepted, that for reasonably sized data sets appropriate quality at interactive rates can be achieved by means of this technique. However, despite these benefits one important issue has received little attention throughout the ongoing discussion of texture based volume rendering: the integration of acceleration techniques to reduce per-fragment operations. In this paper, we address the integration of early ray termination and empty-space skipping into texture based volume rendering on graphical processing units (GPU). Therefore, we describe volume ray-casting on programmable graphics hardware as an alternative to object-order approaches. We exploit the early z-test to terminate fragment processing once sufficient opacity has been accumulated, and to skip empty space along the rays of sight. We demonstrate performance gains up to a factor of 3 for typical renditions of volumetric data sets on the ATI 9700 graphics card.

In this work, the emphasis is on the development of strategies to realize techniques of numerical computing on the graphics chip. In particular, the focus is on the acceleration of techniques for solving sets of algebraic equations as they occur in numerical simulation. We introduce a framework for the implementation of linear algebra operators on programmable graphics processors (GPUs), thus providing the building blocks for the design of more complex numerical algorithms. In particular, we propose a stream model for arithmetic operations on vectors and matrices that exploits the intrinsic parallelism and efficient communication on modern GPUs. Besides performance gains due to improved numerical computations, graphics algorithms benefit from this model in that the transfer of computation results to the graphics processor for display is avoided. We demonstrate the effectiveness of our approach by implementing direct solvers for sparse matrices, and by applying these solvers to multi-dimensional finite difference equations, i.e. the 2D wave equation and the incompressible Navier-Stokes equations.