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This work presents the analysis of data recorded by an eye tracking device in the course of evaluating a foveated rendering approach for head-mounted displays (HMDs). Foveated rendering methods adapt the image synthesis process to the user’s gaze and exploiting the human visual system’s limitations to increase rendering performance. Especially, foveated rendering has great potential when certain requirements have to be fulfilled, like low-latency rendering to cope with high display refresh rates. This is crucial for virtual reality (VR), as a high level of immersion, which can only be achieved with high rendering performance and also helps to reduce nausea, is an important factor in this field. We put things in context by first providing basic information about our rendering system, followed by a description of the user study and the collected data. This data stems from fixation tasks that subjects had to perform while being shown fly-through sequences of virtual scenes on an HMD. These fixation tasks consisted of a combination of various scenes and fixation modes. Besides static fixation targets, moving tar- gets on randomized paths as well as a free focus mode were tested. Using this data, we estimate the precision of the utilized eye tracker and analyze the participants’ accuracy in focusing the displayed fixation targets. Here, we also take a look at eccentricity-dependent quality ratings. Comparing this information with the users’ quality ratings given for the displayed sequences then reveals an interesting connection between fixation modes, fixation accuracy and quality ratings.
In this paper we present the steps towards a well-designed concept of a 5VR6 system for school experiments in scientific domains like physics, biology and chemistry. The steps include the analysis of system requirements in general, the analysis of school experiments and the analysis of input and output devices demands. Based on the results of these steps we show a taxonomy of school experiments and provide a comparison between several currently available devices which can be used for building such a system. We also compare the advantages and shortcomings of 5VR6 and 5AR6 systems in general to show why, in our opinion, 5VR6 systems are better suited for school-use.
Real-Time Simulation of Camera Errors and Their Effect on Some Basic Robotic Vision Algorithms
(2013)
Phase Space Rendering
(2007)
We present the extensible post processing framework GrIP, usable for experimenting with screen space-based graphics algorithms in arbitrary applications. The user can easily implement new ideas as well as add known operators as components to existing ones. Through a well-defined interface, operators are realized as plugins that are loaded at run-time. Operators can be combined by defining a post processing graph (PPG) using a specific XML-format where nodes are the operators and edges define their dependencies. User-modifiable parameters can be manipulated through an automatically generated GUI. In this paper we describe our approach, show some example effects and give performance numbers for some of them.
We present a graph-based framework for post processing filters, called GrIP, providing the possibility of arranging and connecting compatible filters in a directed, acyclic graph for realtime image manipulation. This means that the construction of whole filter graphs is possible through an external interface, avoiding the necessity of a recompilation cycle after changes in post processing. Filter graphs are implemented as XML files containing a collection of filter nodes with their parameters as well as linkage (dependency) information. Implemented methods include (but are not restricted to) depth of field, depth darkening and an implementation of screen space shadows, all applicable in real-time, with manipulable parameterizations.
In contrast to projection-based systems, large, high resolution multi-display systems offer a high pixel density on a large visualization area. This enables users to step up to the displays and see a small but highly detailed area. If the users move back a few steps they don't perceive details at pixel level but will instead get an overview of the whole visualization. Rendering techniques for design evaluation and review or for visualizing large volume data (e.g. Big Data applications) often use computationally expensive ray-based methods. Due to the number of pixels and the amount of data, these methods often do not achieve interactive frame rates.
A view direction based (VDB) rendering technique renders the user's central field of view in high quality whereas the surrounding is rendered with a level-of-detail approach depending on the distance to the user's central field of view. This approach mimics the physiology of the human eye and conserves the advantage of highly detailed information when standing close to the multi-display system as well as the general overview of the whole scene. In this paper we propose a prototype implementation and evaluation of a focus-based rendering technique based on a hybrid ray tracing/sparse voxel octree rendering approach.
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.