Open Access

Despite the ever increasing performance of computer hardware, single machines are not always capable of processing complex tasks in reasonable time, if at all. At the same time, the amount of data to be generated or processed by computer systems increases at a similar rate. Generating images using algorithms from computer graphics is a good example for such scenarios. Because visual analysis of data can greatly benefit from the usage of large, tiled display systems, and techniques to render images become more and more complex in order to generate visually pleasing results, using a single machine is often not sufficient to enable reasonable workflows. In this paper, we introduce CRaP, a framework for cluster-based rendering and postprocessing. Distributing the generation and post-processing of images to a cluster of machines allows to speed up these tasks. In particular, we aim at rendering images at high resolutions, e.g. for large display walls. To our knowledge, no other framework for distributed rendering is capable of postprocessing images in a distributed fashion without being limited to frame tiles. We present a software design that provides a simple interface for creating render sessions, enabling arbitrary applications to delegate the generation of images to a cluster. In addition to a simple configuration process, we intend to support platform interoperability. We focus on the design of a flexible and modular architecture in order to make the framework independent of rendering techniques and scheduling algorithms. Thus, these components should be interchangeable to enable a variety of use cases. The same applies to the integration of postprocessing methods.

We present a system that combines voxel and polygonal representations into a single octree acceleration structure that can be used for ray tracing. Voxels are well-suited to create good level-of-detail for high-frequency models where polygonal simplifications usually fail due to the complex structure of the model. However, polygonal descriptions provide the higher visual fidelity. In addition, voxel representations often oversample the geometric domain especially for large triangles, whereas a few polygons can be tested for intersection more quickly.

The steadily decreasing prices of display technologies and computer graphics hardware contribute to the increasing popularity of multiple-display environments, like large, high-resolution displays. It is therefore necessary that educational organizations give the new generation of computer scientists an opportunity to become familiar with this kind of technology. However, there is a lack of tools that allow for getting started easily. Existing frameworks and libraries that provide support for multi-display rendering are often complex in understanding, configuration and extension. This is critical especially in educational context where the time that students have for their projects is limited and quite short. These tools are also rather known and used in research communities only, thus providing less benefit for future non-scientists. In this work we present an extension for the Unity game engine. The extension allows – with a small overhead – for implementation of applications that are apt to run on both single-display and multi-display systems. It takes care of the most common issues in the context of distributed and multi-display rendering like frame, camera and animation synchronization, thus reducing and simplifying the first steps into the topic. In conjunction with Unity, which significantly simplifies the creation of different kinds of virtual environments, the extension affords students to build mock-up virtual reality applications for large, high-resolution displays, and to implement and evaluate new interaction techniques and metaphors and visualization concepts. Unity itself, in our experience, is very popular among computer graphics students and therefore familiar to most of them. It is also often employed in projects of both research institutions and commercial organizations; so learning it will provide students with qualification in high demand.

Lower back pain is one of the most prevalent diseases in Western societies. A large percentage of European and American populations suffer from back pain at some point in their lives. One successful approach to address lower back pain is postural training, which can be supported by wearable devices, providing real-time feedback about the user’s posture. In this work, we analyze the changes in posture induced by postural training. To this end, we compare snapshots before and after training, as measured by the Gokhale SpineTracker™. Considering pairs of before and after snapshots in different positions (standing, sitting, and bending), we introduce a feature space, that allows for unsupervised clustering. We show that resulting clusters represent certain groups of postural changes, which are meaningful to professional posture trainers.

In presence of conflicting or ambiguous visual cues in complex scenes, performing 3D selection and manipulation tasks can be challenging. To improve motor planning and coordination, we explore audio-tactile cues to inform the user about the presence of objects in hand proximity, e.g., to avoid unwanted object penetrations. We do so through a novel glove-based tactile interface, enhanced by audio cues. Through two user studies, we illustrate that proximity guidance cues improve spatial awareness, hand motions, and collision avoidance behaviors, and show how proximity cues in combination with collision and friction cues can significantly improve performance.

This article describes an approach to rapidly prototype the parameters of a Java application run on the IBM J9 Virtual Machine in order to improve its performance. It works by analyzing VM output and searching for behavioral patterns. These patterns are matched against a list of known patterns for which rules exist that specify how to adapt the VM to a given application. Adapting the application is done by adding parameters and changing existing ones. The process is fully automated and carried out by a toolkit. The toolkit iteratively cycles through multiple possible parameter sets, benchmarks them and proposes the best alternative to the user. The user can, without any prior knowledge about the Java application or the VM improve the performance of the deployed application and quickly cycle through a multitude of different settings to benchmark them. When tested with the representative benchmarks, improvements of up to 150% were achieved.

Touchscreen interaction suffers from occlusion problems as fingers can cover small targets, which makes interacting with such targets challenging. To improve touchscreen interaction accuracy and consequently the selection of small or hidden objects we introduce a back-of-device force feedback system for smartphones. We introduce a new solution that combines force feedback on the back to enhance touch input on the front screen. The interface includes three actuated pins at the back of a smartphone. All three pins are driven by micro servos and can be actuated up to a frequency of 50Hz and a maximum amplitude of 5mm. In a first psychophysical user study, we explored the limits of the system. Thereafter, we demonstrate through a performance study that the proposed interface can enhance touchscreen interaction precision, compared to state-of-the-art methods. In particular the selection of small targets performed remarkably well with force feedback. The study additionally shows that users subjectively felt significantly more accurate with force feedback. Based on the results, we discuss back-to-front feedback design issues and demonstrate potential applications through several prototypical concepts to illustrate where the back-of-device force feedback could be beneficial.

We propose a high-performance GPU implementation of Ray Histogram Fusion (RHF), a denoising method for stochastic global illumination rendering. Based on the CPU implementation of the original algorithm, we present a naive GPU implementation and the necessary optimization steps. Eventually, we show that our optimizations increase the performance of RHF by two orders of magnitude when compared to the original CPU implementation and one order of magnitude compared to the naive GPU implementation. We show how the quality for identical rendering times relates to unfiltered path tracing and how much time is needed to achieve identical quality when compared to an unfiltered path traced result. Finally, we summarize our work and describe possible future applications and research based on this.

Large, high-resolution displays demonstrated their effectiveness in lab settings for cognitively demanding tasks in single user and collaborative scenarios. The effectiveness is mostly reached through inherent displays' properties - large display real estate and high resolution - that allow for visualization of complex datasets, and support of group work and embodied interaction. To raise users' efficiency, however, more sophisticated user support in the form of advanced user interfaces might be needed. For that we need profound understanding of how large, tiled displays impact users work and behavior. We need to extract behavioral patterns for different tasks and data types. This paper reports on study results of how users, while working collaboratively, process spatially fixed items on large, tiled displays. The results revealed a recurrent pattern showing that users prefer to process documents column wise rather than row wise or erratic.