Tom Vierjahn

Surface-reconstructing growing neural gas

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 objects 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. Graphical abstractDisplay Omitted HighlightsOnline learning of triangulated surface with concurrent visualization.SGNG adapts to modifications of input data at any time during reconstruction.SGNG assigns textures automatically avoiding occlusion artifacts.

Remain seated: towards fully-immersive desktop VR

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.

Turning anonymous members of a multiagent system into individuals

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 agents behavioral design modeling the transition between an anonymous and an individual agent when a user approaches.

Utilizing immersive virtual reality in everydaywork

Applications of Virtual Reality (VR) have been repeatedly explored with the goal to improve the data analysis process of users from different application domains, such as architecture and simulation sciences. Unfortunately, making VR available in professional application scenarios or even using it on a regular basis has proven to be challenging. We argue that everyday usage environments, such as office spaces, have introduced constraints that critically affect the design of interaction concepts since well-established techniques might be difficult to use. In our opinion, it is crucial to understand the impact of usage scenarios on interaction design, to successfully develop VR applications for everyday use. To substantiate our claim, we define three distinct usage scenarios in this work that primarily differ in the amount of mobility they allow for. We outline each scenario’s inherent constraints but also point out opportunities that may be used to design novel, well-suited interaction techniques for different everyday usage environments. In addition, we link each scenario to a concrete application example to clarify its relevance and show how it affects interaction design.

Correlating sub-phenomena in performance data in the frequency domain

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.

Growing Cell Structures Learning a Progressive Mesh During Surface Reconstruction - A Top-Down Approach

Growing Cell Structures (GCS) have been proven to be suitable for surface reconstruction from unstructured point clouds. The reconstructed triangle mesh can be represented compactly as a progressive mesh with integrated level of detail by storing only vertex split operations. However, half-edge collapse operations are used for GCS. In this paper, we present an improvement to a GCS-based surface reconstruction technique by converting a halfedge collapse to a more general vertex removal to create a progressive mesh. We have evaluated the new technique with respect to running time overhead and mesh quality. Results indicate that this technique can be used for efficient surface reconstruction. We will use the presented findings as basis for future research.

Score-based recommendation for efficiently selecting individual virtual agents in multi-agent systems

Controlling user-agent-interactions by means of an external operator includes selecting the virtual interaction partners fast and faultlessly. However, especially in immersive scenes with a large number of potential partners, this task is non-trivial. Thus, we present a score-based recommendation system supporting an operator in the selection task. Agents are recommended as potential partners based on two parameters: the users distance to the agents and the users gazing direction. An additional graphical user interface (GUI) provides elements for configuring the system and for applying actions to those agents which the operator has confirmed as interaction partners.

Towards a design space characterizing workflows that take advantage of immersive visualization

Immersive visualization (IV) fosters the creation of mental images of a data set, a scene, a procedure, etc. We devise an initial version of a design space for categorizing workflows that take advantage of IV. From this categorization, specific requirements for an actual, seamless IV-integration can be derived. We validate the design space with three workflows investigated in our research projects.

Evaluating Presence Strategies of Temporarily Required Virtual Assistants

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.

Geometry-aware visualization of performance data

Phenomena in the performance behaviour of high-performance computing (HPC) applications can stem from the HPC system itself, from the applications 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.