Publication List

The Research Cluster "Smart Communities and Technologies" (Smart CT) at TU Wien will provide the scientific underpinnings for next-generation complex smart city and communities infrastructures. Cities are ever-evolving, complex cyber physical systems of systems covering a magnitude of different areas. The initial concept of smart cities and communities started with cities utilizing communication technologies to deliver services to their citizens and evolved to using information technology to be smarter and more efficient about the utilization of their resources. In recent years however, information technology has changed significantly, and with it the resources and areas addressable by a smart city have broadened considerably. They now cover areas like smart buildings, smart products and production, smart traffic systems and roads, autonomous driving, smart grids for managing energy hubs and electric car utilization or urban environmental systems research.

3D spatialization creates the link between the internet of cities infrastructure and the actual 3D world in which a city is embedded in order to perform advanced computation and visualization tasks. Sensors, actuators and users are embedded in a complex 3D environment that is constantly changing. Acquiring, modeling and visualizing this dynamic 3D environment are the challenges we need to face using methods from Visual Computing and Computer Graphics. 3D Spatialization aims to make a city aware of its 3D environment, allowing it to perform spatial reasoning to solve problems like visibility, accessibility, lighting, and energy efficiency.

The VRVis K1 Research Center is the leading application oriented research center in the area of virtual reality (VR) and visualization (Vis) in Austria and is internationally recognized. You can find extensive Information about the VRVis-Center here

Point clouds are a quintessential 3D geometry representation format, and often the first model obtained from reconstructive efforts, such as LIDAR scans. IVILPC aims for fast, authentic, interactive, and high-quality processing of such point-based data sets. Our project explores high-performance software rendering routines for various point-based primitives, such as point sprites, gaussian splats, surfels, and particle systems. Beyond conventional use cases, point cloud rendering also forms a key component of point-based machine learning methods and novel-view synthesis, where performance is paramount. We will exploit the flexibility and processing power of cutting-edge GPU architecture features to formulate novel, high-performance rendering approaches. The envisioned solutions will be applicable to unstructured point clouds for instant rendering of billions of points. Our research targets minimally-invasive compression, culling methods, and level-of-detail techniques for point-based rendering to deliver high performance and quality on-demand. We explore GPU-accelerated editing of point clouds, as well as common display issues on next-generation display devices. IVILPC lays the foundation for interaction with large point clouds in conventional and immersive environments. Its goal is an efficient data knowledge transfer from sensor to user, with a wide range of use cases to image-based rendering, virtual reality (VR) technology, architecture, the geospatial industry, and cultural heritage.

This research project focuses on 3D motion analysis and motion learning methodologies. We design novel methods for automated analysis of human motion by machine learning. These methods can be applicable in real training scenario or in VR training setup. The results of our motion analysis can help players better understand the errors in their motion and lead to improvement of motion performance. Our motion analysis methods are based on professional knowledge from tennis experts from our partner company VR Motion Learning GmbH & Co KG. We use numerous motion features, including rotations, positions, velocities and others, to analyze the motion.

Our goal is to use virtual reality as scenario for learning correct tennis technique that will be applicable in real tennis game. For this purpose, we plan to join our motion analysis with error visualization techniques in 3D and with novel motion learning methodologies. These methodologies may lead to learning correct sport technique, improvement of performance and prevention of injuries.

Industrial Building Design is a design process where the successful implementation of each project is based on collaborative decision making of multiple domain specialists - architects, engineers, production system planners and building owners. Traditionally, such multi-collaborator workflows are subject to conflicting stakeholder goals and frequent changes in production processes inevitably resulting in lengthy planning periods. This particular design process needs novel approaches to decision-making support which would combine the ability to communicate design intent with real-time feedback on the impact of design decisions.

BimFlexi project aims to accelerate BIM design processes for industrial buildings by using parametric modelling, multi-parameter optimization and collaborative VR exploration and modification of models at early stages of building planning.

This is a joint project with the civil engineering faculty and several companies. Its aim is the development of an Integrated Framework “Housing 4.0”; a digital platform supporting integrated planning and project delivery through coupling various digital tools and databases, like Building Information Modeling (BIM) for Design to Production  and Parametric Habitat Designer.

Our goal is to exploit the potential of BIM for modular, off-site housing assembly in order to improve planning and construction processes, reduce cost and construction time and allow for mass customization will be explored.

The novel approach in this project is user-involvement; which has been neglected in recent national and international projects on off-site, modular construction supported by digital technologies. A parametric design tool should allow different stakeholders to explore both high-level and low-level options and their impact on the construction project so that mutually optimal solutions can be found easier.

The central focus of our research is to understand visual abstraction. Understanding means 1. to identify meaningful visual abstractions, 2. to assess their effectiveness for human perception and cognition and 3. to formalize them to be executable on a computational machinery. The outcome of the investigation is useful for designing visualizations for a given scenario or need, whose effectiveness can be quantified and thus the most understandable visualization design can be effortlessly determined. The science of visualization has already gained some understanding of structural visual abstraction. When for example illustrators, artists, and visualization designers convey certain structure, or visually express how things look, we can often provide a scientifically-founded argument whether and why is their expression effective for human cognitive processing. What has not been given sufficient scientific attention to, is advancing the understanding of procedural visual abstraction, in other words investigating visual means that convey what things do or how things work. This missing piece of knowledge would be very useful for visual depiction of processes and dynamics that are omnipresent in science, technology, but also in our everyday lives. The upcoming project will therefore investigate theoretical foundations for visualization of processes. Physiological processes that describe the complex machinery of biological life will be picked as a target scenario. The reason for this choice is two-fold. Firstly, these processes are immensely complex, are carried-out on various spatial and temporal levels simultaneously, and can be sufficiently understood only if all scales are considered. Secondly, physiological processes have been modeled as a result of intensive research in biology, systems biology, and biochemistry and are available in a form of digital data. The goal will be to visually communicate how physiological processes participate on life by considering the limitations of human perceptual and cognitive capabilities. By solving individual visualization problems of this challenging target scenario, the research will provide first pieces of understanding of procedural visual abstractions that are generally applicable, beyond the chosen target domain. Prototype implementation of the developed technology is available at the GitHub repository: https://github.com/illvisation/

Prototype implementation of the developed technology is available at the GitHub repository:

https://github.com/illvisation/

cellVIEW

cellVIEW is a new tool that provides fast rendering of very large biological macromolecular scenes and is inspired by state-of-the-art computer graphics techniques. Click here for additional information.

Invited Talks

18.11.2016: Arthur J. Olson, Envisioning the Visible Molecular Cell

17.10.2016: Kwan-Liu Ma, Emerging Topics for Visualization Research: Part1, Part2

07.10.2016: Marc Streit, From Visual Exploration to Storytelling and Back Again

04.12.2015: Jan Palacek, Visual Analysis of Protein Complexes: From Protein Interaction to Cellular Processes

19.04.2013: Jan Koenderink, Shape in Visual Awareness