Dr. Tom Vierjahn|
Phone: +49 241 80 29716
Fax: +49 241 80 22134
Office hours: Mondays, 2 - 3 p.m. Please make an appointment via mail.
Dissertation: Online Surface Reconstruction From Unorganized Point Clouds With Integrated Texture Mapping, Westfälische Wilhelmsuniversität Münster, 2015
In this work we describe the scenario of fully-immersive desktop VR, which serves the overall goal to seamlessly integrate with existing workflows and workplaces of data analysts and researchers, such that they can benefit from the gain in productivity when immersed in their data-spaces. Furthermore, we provide a literature review showing the status quo of techniques and methods available for realizing this scenario under the raised restrictions. Finally, we propose a concept of an analysis framework and the decisions made and the decisions still to be taken, to outline how the described scenario and the collected methods are feasible in a real use case.
It is increasingly common to embed embodied, human-like, virtual agents into immersive virtual environments for either of the two use cases: (1) populating architectural scenes as anonymous members of a crowd and (2) meeting or supporting users as individual, intelligent and conversational agents. However, the new trend towards intelligent cyber physical systems inherently combines both use cases. Thus, we argue for the necessity of multiagent systems consisting of anonymous and autonomous agents, who temporarily turn into intelligent individuals. Besides purely enlivening the scene, each agent can thus be engaged into a situation-dependent interaction by the user, e.g., into a conversation or a joint task. To this end, we devise components for an agent’s behavioral design modeling the transition between an anonymous and an individual agent when a user approaches.
Embodied, virtual agents provide users assistance in agent-based support systems. To this end, two closely linked factors have to be considered for the agents’ behavioral design: their presence time (PT), i.e., the time in which the agents are visible, and the approaching time (AT), i.e., the time span between the user’s calling for an agent and the agent’s actual availability.
This work focuses on human-like assistants that are embedded in immersive scenes but that are required only temporarily. To the best of our knowledge, guidelines for a suitable trade-off between PT and AT of these assistants do not yet exist. We address this gap by presenting the results of a controlled within-subjects study in a CAVE. While keeping a low PT so that the agent is not perceived as annoying, three strategies affecting the AT, namely fading, walking, and running, are evaluated by 40 subjects. The results indicate no clear preference for either behavior. Instead, the necessity of a better trade-off between a low AT and an agent’s realistic behavior is demonstrated.
Understanding the performance behaviour of high-performance computing (hpc) applications based on performance profiles is a challenging task. Phenomena in the performance behaviour can stem from the hpc system itself, from the application’s code, but also from the simulation domain. In order to analyse the latter phenomena, we propose a system that visualizes profile-based performance data in its spatial context in the simulation domain, i.e., on the geometry processed by the application. It thus helps hpc experts and simulation experts understand the performance data better. Furthermore, it reduces the initially large search space by automatically labeling those parts of the data that reveal variation in performance and thus require detailed analysis.
Understanding the performance behaviour of massively parallel high-performance computing (HPC) applications based on call-path performance profiles is a time-consuming task. In this paper, we introduce the concept of directed variance in order to help analysts find performance bottlenecks in massive performance data and in the end optimize the application. According to HPC experts’ requirements, our technique automatically detects severe parts in the data that expose large variation in an application’s performance behaviour across system resources. Previously known variations are effectively filtered out. Analysts are thus guided through a reduced search space towards regions of interest for detailed examination in a 3D visualization. We demonstrate the effectiveness of our approach using performance data of common benchmark codes as well as from actively developed production codes.
Finding and understanding correlated performance behaviour of the individual functions of massively parallel high-performance computing (HPC) applications is a time-consuming task. In this poster, we propose filtered correlation analysis for automatically locating interdependencies in call-path performance profiles. Transforming the data into the frequency domain splits a performance phenomenon into sub-phenomena to be correlated separately. We provide the mathematical framework and an overview over the visualization, and we demonstrate the effectiveness of our technique.
Best Poster Award!
Computer-controlled virtual humans can serve as assistants in virtual scenes. Here, they are usually in an almost constant contact with the user. Nonetheless, in some applications assistants are required only temporarily. Consequently, presenting them only when needed, i.e, minimizing their presence time, might be advisable.
To the best of our knowledge, there do not yet exist any design guidelines for such agent-based support systems. Thus, we plan to close this gap by a controlled qualitative and quantitative user study in a CAVE-like environment.We expect users to prefer assistants with a low presence time as well as a low fallback time to get quick support. However, as both factors are linked, a suitable trade-off needs to be found. Thus, we plan to test four different strategies, namely fading, moving, omnipresent and busy. This work presents our hypotheses and our planned within-subject design.
Phenomena in the performance behaviour of high-performance computing (HPC) applications can stem from the HPC system itself, from the application's code, but also from the simulation domain. In order to analyse the latter phenomena, we propose a system that visualizes profile-based performance data in its spatial context, i.e., on the geometry, in the simulation domain. It thus helps HPC experts but also simulation experts understand the performance data better. In addition, our tool reduces the initially large search space by automatically labelling large-variation views on the data which require detailed analysis.
Virtual Agents (VAs) are embedded in virtual environments for two reasons: they enliven architectural scenes by representing more realistic situations, and they are dialogue partners. They can function as training partners such as representing students in a teaching scenario, or as assistants by, e.g., guiding users through a scene or by performing certain tasks either individually or in collaboration with the user. However, designing such VAs is challenging as various requirements have to be met. Two relevant factors will be briefly discussed in the talk: Collision Avoidance and Presence Strategies.
In this paper we propose surface-reconstructing growing neural gas (SGNG), a learning based artificial neural network that iteratively constructs a triangle mesh from a set of sample points lying on an object?s surface. From these input points SGNG automatically approximates the shape and the topology of the original surface. It furthermore assigns suitable textures to the triangles if images of the surface are available that are registered to the points.
By expressing topological neighborhood via triangles, and by learning visibility from the input data, SGNG constructs a triangle mesh entirely during online learning and does not need any post-processing to close untriangulated holes or to assign suitable textures without occlusion artifacts. Thus, SGNG is well suited for long-running applications that require an iterative pipeline where scanning, reconstruction and visualization are executed in parallel.
Results indicate that SGNG improves upon its predecessors and achieves similar or even better performance in terms of smaller reconstruction errors and better reconstruction quality than existing state-of-the-art reconstruction algorithms. If the input points are updated repeatedly during reconstruction, SGNG performs even faster than existing techniques.
Best Paper Award!