Simulating Wind and Warmth in Virtual Reality: Conception, Realization and Evaluation for a CAVE Environment

Wind and warmth sensations proved to be able to enhance users' state of presence in Virtual Reality applications. Still, only few projects deal with their detailed effect on the user and general ways of implementing such stimuli. This work tries to fill this gap: After analyzing requirements for hardware and software concerning wind and warmth simulations, a hardware and also a software setup for the application in a CAVE environment is proposed. The setup is evaluated with regard to technical details and requirements, but also - in the form of a pilot study - in view of user experience and presence. Our setup proved to comply with the requirements and leads to satisfactory results. To our knowledge, the low cost simulation system (approx. 2200 Euro) presented here is one of the most extensive, most flexible and best evaluated systems for creating wind and warmth stimuli in CAVE-based VR applications.

A Comparative Evaluation of Three Skin Color Detection Approaches

Skin segmentation is a challenging task due to several influences such as unknown lighting conditions, skin colored background, and camera limitations. A lot of skin segmentation approaches were proposed in the past including adaptive (in the sense of updating the skin color online) and non-adaptive approaches. In this paper, we compare three skin segmentation approaches that are promising to work well for hand tracking, which is our main motivation for this work. Hand tracking can widely be used in VR/AR e.g. navigation and object manipulation. The first skin segmentation approach is a well-known non-adaptive approach. It is based on a simple, pre-computed skin color distribution. Methods two and three adaptively estimate the skin color in each frame utilizing clustering algorithms. The second approach uses a hierarchical clustering for a simultaneous image and color space segmentation, while the third approach is a pure color space clustering, but with a more sophisticated clustering approach. For evaluation, we compared the segmentation results of the approaches against a ground truth dataset. To obtain the ground truth dataset, we labeled about 500 images captured under various conditions.

Influence of Comfort on 3D Selection Task Performance in Immersive Desktop Setups

Immersive virtual environments (IVEs) have the potential to afford natural interaction in the three-dimensional (3D) space around a user. However, interaction performance in 3D mid-air is often reduced and depends on a variety of ergonomics factors, the user's endurance, muscular strength, as well as fitness. In particular, in contrast to traditional desktop-based setups, users often cannot rest their arms in a comfortable pose during the interaction. In this article we analyze the impact of comfort on 3D selection tasks in an immersive desktop setup. First, in a pre-study we identified how comfortable or uncomfortable specific interaction positions and poses are for users who are standing upright. Then, we investigated differences in 3D selection task performance when users interact with their hands in a comfortable or uncomfortable body pose, while sitting on a chair in front of a table while the VE was displayed on a headmounted display (HMD). We conducted a Fitts' Law experiment to evaluate selection performance in different poses. The results suggest that users achieve a significantly higher performance in a comfortable pose when they rest their elbow on the table.

Modell und Wirklichkeit

Die Bildserie zeigt eine Reihe von architektonischen Einfügungen in verschiedene räumliche Umgebungen. Den Arbeiten ist gemein, dass sie allesamt aus einer Auseinandersetzung mit den Sprachen des Modellhaften heraus entwickelt worden sind. Der Akt des konkreten Einfügens stand jeweils am Ende einer längeren Reflexion über die Rolle des Modells im architektonischen Entwurfsprozess.

Influence of Information and Instructions on Human Behavior in Tunnel Accidents: A Virtual Reality Study

Human behavior is a major factor modulating the consequences of road tunnel accidents. We investigated the effect of information and instruction on drivers' behavior as well as the usability of virtual environments to simulate such emergency situations. Tunnel safety knowledge of the general population was assessed using an online questionnaire, and tunnel safety behavior was investigated in a virtual reality experiment. Forty-four participants completed three drives through a virtual road tunnel and were confronted with a traffic jam, no event, and an accident blocking the road. Participants were randomly assigned to a control group (no intervention), an informed group who read a brochure containing safety information prior to the tunnel drives, or an informed and instructed group who read the same brochure and received additional instructions during the emergency situation. Informed participants showed better and quicker safety behavior than the control group. Self-reports of anxiety were assessed three times during each drive. Anxiety was elevated during and after the emergency situation. The findings demonstrate problematic safety behavior in the control group and that knowledge of safety information fosters adequate behavior in tunnel emergencies. Enhanced anxiety ratings during the emergency situation indicate external validity of the virtual environment.

Advanced luminance control and black offset correction for multi-projector display systems

In order to display a homogeneous image using multiple projectors, differences in the projected intensities must be compensated. In this paper, we present novel approaches to combine and extend existing techniques for edge blending and luminance harmonization to achieve a detailed luminance control. Furthermore, we apply techniques for improving the contrast ratio of multi-segmented displays also to the black offset correction. We also present a simple scheme to involve the displayed context in the correction process to dynamically improve the contrast in brighter images. In addition, we present a metric to evaluate the different methods and their influence on the visual quality.

Real-time depth camera tracking with CAD models and ICP

In recent years, depth cameras have been widely utilized in camera tracking for augmented and mixed reality. Many of the studies focus on the methods that generate the reference model simultaneously with the tracking and allow operation in unprepared environments. However, methods that rely on predefined CAD models have their advantages. In such methods, the measurement errors are not accumulated to the model, they are tolerant to inaccurate initialization, and the tracking is always performed directly in reference model's coordinate system. In this paper, we present a method for tracking a depth camera with existing CAD models and the Iterative Closest Point (ICP) algorithm. In our approach, we render the CAD model using the latest pose estimate and construct a point cloud from the corresponding depth map. We construct another point cloud from currently captured depth frame, and find the incremental change in the camera pose by aligning the point clouds. We utilize a GPGPU-based implementation of the ICP which efficiently uses all the depth data in the process. The method runs in real-time, it is robust for outliers, and it does not require any preprocessing of the CAD models. We evaluated the approach using the Kinect depth sensor, and compared the results to a 2D edge-based method, to a depth-based SLAM method, and to the ground truth. The results show that the approach is more stable compared to the edge-based method and it suffers less from drift compared to the depth-based SLAM.