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Schlussbericht HIGEDIS
(2015)
Dieser Tagungsband enthält die Beiträge zum 12. Workshop zum Thema Virtuelle und Erweiterte Realität der Fachgruppe VR/AR der Gesellschaft für Informatik e.V. Der Workshop dient zum Informations- und Ideenaustausch deutschsprachigen WissenschaftlerInnen, zusätzlich bietet der Workshop den idealen Rahmen aktuelle Ergebnisse und Vorhaben aus Forschung und Entwicklung einem fachkundigen Publikum zur Diskussion zu stellen. Insbesondere wollen wir auch jungen Nachwuchswissenschaftlern die Möglichkeit geben, ihre Arbeiten zu präsentieren.
The study of locomotion in virtual environments is a diverse and rewarding research area. Yet, creating effective and intuitive locomotion techniques is challenging, especially when users cannot move around freely. While using handheld input devices for navigation may often be good enough, it does not match our natural experience of motion in the real world. Frequently, there are strong arguments for supporting body-centered self-motion cues as they may improve orientation and spatial judgments, and reduce motion sickness. Yet, how these cues can be introduced while the user is not moving around physically is not well understood. Actuated solutions such as motion platforms can be an option, but they are expensive and difficult to maintain. Alternatively, within this article we focus on the effect of upper-body tilt while users are seated, as previous work has indicated positive effects on self-motion perception. We report on two studies that investigated the effects of static and dynamic upper body leaning on perceived distances traveled and self-motion perception (vection). Static leaning (i.e., keeping a constant forward torso inclination) had a positive effect on self-motion, while dynamic torso leaning showed mixed results. We discuss these results and identify further steps necessary to design improved embodied locomotion control techniques that do not require actuated motion platforms.
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.