Ongoing Projects

Digital data on tangible and intangible cultural assets is an essential part of daily life, communication and experience. It has a lasting influence on the perception of cultural identity as well as on the interactions between research, the cultural economy and society. Throughout the last three decades, many cultural heritage institutions have contributed to a wealth of digital representations of cultural assets (2D digital reproductions of paintings, sheet music, 3D digital models of sculptures, monuments, rooms, buildings), audio-visual data (music, film, stage performances), and procedural research data such as encoding and annotation formats. The long-term preservation and FAIR availability of research data from the cultural heritage domain is fundamentally important, not only for future academic success in the humanities but also for the cultural self-understanding of individuals and society as a whole. Up to now, no coordinated effort for professional research data management on a national level exists. NFDI4Culture aims to fill this gap and create a user-centred, research-driven infrastructure that will cover a broad range of research domains from musicology, art history and architecture to performance, theatre, film, and media studies.
In this project, we aim to develop the technology that lays the foundation for applications that require the anticipation of human behavior. Instead of addressing the problem at a limited scope, the project addresses all relevant aspects including time horizons ranging from milliseconds to infinity and granularity ranging from detailed human motion to coarse action labels.
In this project we take image-based reflectance measurements, which are subsequently processed into high quality spatially varying BRDFs (SVBRDFs). We develop alternative approaches to the SVBRDF fitting problem with the help of deep learning.
The image-based acquisition of complex optical material properties is one of the major research topics in our group. The goal of this project is the development of novel techniques for the efficient and high-fidelity capture of high-dimensional material representations like, e.g., the bidirectional texture function (BTF). Example data is publicly available at the BTF database Bonn.
In this project an interactive visual approach to shape analysis of 3D structures is taken. As concrete application serves here the analysis of the skull morphology of European mice and rats based on high-resolution 3D scans.
In this project we strive to derive a statistical model of the space spanned by a database of measured BTFs. This way, we intend to develop a dramatically more general representation of materials than is currently available. The goal is to reparameterize the high-dimensional material space to allow perceptually meaningful interpolations between the acquired samples, i.e., to generate new materials that blend qualities of samples from the dataset.
The goal of the project is the anticipation of full body motions. Combining purely data-driven with physics-based modeling approaches, anticipation of full body motions on the basis of very sparse sensor signals of different nature, e.g., inertial measurement units, EMG sensors, or ground-contact sensors, will be realized. Extending the information on the physics-based layer by model-based anticipation components (including information from balance control, physical constraints) is another important objective to allow robust extrapolations from the range of motions similar to ones recorded in an existing knowledge base to new motion ranges, especially those relatedto disabilities. The anticipated whole-body motions can be used to determine the optimal robot placement for collaborative tasks and for direct entrainment and modulation of ongoing motor behavior. Symbolic labels and trajectories from affordances obtained from other projects of the research unit will be incorporated as additional a priori knowledge on motions, which will reduce computations times, will stabilize short term predictions and even open the door for the method to long-term anticipations.
SYMBIONT is an interdisciplinary project ranging from mathematics via computer science to systems biology and systems medicine. The project has a clear focus on fundamental research on mathematical methods, and prototypes in software, which is in turn benchmarked against models from computational biology databases. Computational models in systems biology are built from molecular interaction networks and rate parameters resulting in large systems of differential equations. These networks are foundational for systems medicine. The currently prevailing numerical approaches shall be complemented with our novel algorithmic symbolic methods, which will address fundamental problems in this area. One important problem is that statistical estimation of model parameters is computationally expensive and many parameters are not identifiable from experimental data. In addition, there is typically a considerable uncertainty about the exact form of the mathematical model itself. The parametric uncertainty (with wide potential variations of parameters by several orders of magnitudes) leads to severe limitations of numerical approaches even for rather small and low dimensional models. Furthermore, extant model inference and analysis methods suffer from the curse of dimensionality that sets an upper limit of about ten variables to the tractable models. For those reasons, the formal deduction of principle properties of large and very large models has a very high relevance. The main goal of SYMBIONT is to combine symbolic methods with model reduction methods for the analysis of biological networks. We propose new methods for symbolic analysis, which overcome the above mentioned obstacles and therefore can be applied to large networks. In order to cope more effectively with the parameter uncertainty problem, we impose an entirely new paradigm replacing thinking about single instances with thinking about orders of magnitude. Our computational methods are diverse and involve various branches of mathematics such as tropical geometry, real algebraic geometry, theories of singular perturbations, invariant manifolds and symmetries of differential systems. The foundations and validity of our methods will be carefully secured by mathematical investigation. Corresponding computer algebra problems are NP-hard, but experiments point at their feasibility for biological networks. We have already shown that complexity parameters such as tree-width or number of distinct metastable regimes grow only slowly with size for models available in existing biological databases. We will exploit this observation to solve challenging problems in network analysis including determination of parameter regions for the existence and stability of attractors, model reduction, and characterization of qualitative dynamics of nonlinear networks. The methods developed in this project will be benchmarked against existing biological models and also against more challenging models, closer to the needs of systems and precision medicine that will be generated using biological pathways databases.
The goal of this project is to develop range imaging setups and reconstruction techniques that are not only robust to multi-path scattering, but use multi-path contributions as an additional source of information. This requires the development of advanced image formation models, and methods to solve the corresponding inverse problems.
Physically-based analysis and synthesis of (human) motions have a number of applications. They can help to enhance the efficiency of medical rehabilitation, to improve the understanding of motions in the realm of sports or to generate realistic animations for movies and computer games.
On this page, we want to introduce you to our research in the field of sonification, partially carried out in cooperation with the Institute of Sport-science and Sports at the University of Bonn and the University of Hannover.
3D acquisition devices usually produce unstructured point-clouds as primary output. A challenge in this context is the decomposition of the point-cloud data into known parts in order to introduce abstractions of the originally unorganized data. This information can be used for compression, recognition and reconstruction.

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Completed Projects

Our group deals with the efficient representation, management and visualization of 3D surface data that gets captured incrementally by an autonomously flying drone. This data will be integrated into a global 3D map.
Motion capture systems allow for tracking and recording human motions at high spatial and temporal resolutions. However, we are interested in alternative techniques getting along with far less input data.
This project aims to advance modelling methods applied in procedural modeling by analyzing how existing approaches to model specific types of models or generic procedural modeling approaches can be improved.
The goal of this project is markerless and real-time tracking a human hand (position, orientation and joint angles) based on computer vision in order to enable natural and efficient human computer interaction.
This project aims at methods for view-dependent realtime visualisation of city models with details up to single cm close to the viewer. Apart from supporting the realtime visualisation the employed LoD hierarchies should also support accentuation and abstraction of semantic information.
Within the Research Training Group 437 "Landform - a structured and variable boundary layer" Ph.D. students of various scientific branches work together to help understand the role of the landform in geo-systems.
Car paint and especially metallic or pearlescent paints pose serious challenges to computer graphics. This is due to their high dynamic range, their high frequent changes of reflectance both in angular and in spatial domain as well as the angular dependent color shift behaviour of pearlescent paints which is not covered by commonplace reflectance models. In the Car Paint Project we develop new compression, rendering and editing techniques for all kinds of car paints.
The MERCW project focuses on the study of the dumpsites of chemical weapons from the Second World War located in the Baltic Sea and the assessment of the resulting potential threat to the marine environment and the population. Within the scope of this project, we are doing research on visualization techniques for suitable interactive exploration of the heterogeneous project relevant data.
In the scope of the Last-Mile Project we are carrying out research on interactive visualisation of potential tsunami hazards via networks.
We will analyse bifurcations and singularities of algebraic systems of ordinary differential equations with particular emphasis on questions concerning the existence of oscillations.
The primary objective of the hair research at University of Bonn is the development of a high accuracy model for human hair simulation. This includes hair style modeling and physical based hair dynamics simulation as well as hair rendering.
In the context of OpenSG Plus the most important german research institutions in the field of 3D computer graphics want to bundle their capabilities to support OpenSG and improve it by forward-looking functionality.
In the COHERENT project, six leading European organisations provide complementary competencies to create a new networked holographic audio-visual platform to support real-time collaborative 3D interaction between geographically distributed teams.
The tremendous amount of data acquired via remote sensing or airborne sensors provide the basis for a photorealistic rendering of ample landscapes, even from close-by viewpoints. This projects aims at a realtime exploration of such arbitrary-sized datasets without sacrificing visible detail.
The goal of this project is to elaborate a set of algorithms and methods that will allow our system to intelligently compare 3D objects in a way that is close to the human notion of resemblance. As a further step, these results will be used to automatically search in 3D Digital Libraries.
EPOCH is a network of about a hundred European cultural institutions joining their efforts to improve the quality and effectiveness of the use of Information and Communication Technology for Cultural Heritage. Participants include university departments, research centres, heritage institutions, such as museums or national heritage agencies, and commercial enterprises, together endeavouring to overcome the fragmentation of current research in this field.
The RealReflect project’s goal is to increase the realism of Virtual Reality (VR) technology by developing physically correct visualization technology capable of accounting for phenomena like metamerism, fluorescence and light polarization and integrating it into an existing VR system. This will enable users from many different areas like automotive industry or architecture to create VR simulations for the interior design, thereby avoiding the necessity to build expensive real prototypes which reduces costs as well as time to market of the overall end products.
The goal of the project is the creation of the technological bases for a synergetic connection of the innovative service of clothing for the individual customer (tailor-made suit) with the potential of the E-Commerce business using Virtual-Reality (VR) methods.
The goal of the PROBADO project is to develop tools and systems that allow academic libraries to treat different common documents in the same way as textual documents. Amongst other document types, the project's focus is on 3D-models stemming from the architectural domain. Thereby, the major task is to develop appropriate searching and classification methods for such 3D objects.
The european union funded project 3D-COFORM (3D-COllection-FORMation) deals with the development of novel techniques for digitising objects from the cultural heritage area. The goal is to digitise such objects more efficiently and with better quality compared to the current state-of-the-art. This way 3D-documentation will become an everyday practical choice for digital documentation campaigns in the cultural heritage sector.
The goal of the DURAARKK project is the development of tools and systems that allow sustainable long-term archival of digital 3D architectural data. It thereby supports a large variety of representations, starting with legacy CAD models over 3D point cloud data up to state of the art Building Information Modeling (BIM) documents.
Instead of a goal-driven acquisition that determines the devices and sensors, we let the sensors and resulting available data determine the acquisition process. Data acquisition might become incidental to other tasks that devices/People to which sensors are attached carry out. A variety of challenging problems need to be solved to exploit this huge amount of data, including: dealing with continuous streams of time-dependent data, finding means of integrating data from different sensors and modalities, detecting changes in data sets to create 4D models, harvesting data to go beyond simple 3D geometry, and researching new paradigms for interactive inspection capabilities with 4D data sets. In this project, we envision solutions to these challenges, paving the way for affordable and innovative uses of information technology in an evolving world sampled by ubiquitous visual sensors.
Motion capture systems allow for tracking and recording human motions at high spatial and temporal resolutions. However, we are interested in alternative techniques getting along with far less input data, such as uncalibrated monocular video.
Motion capturing has become a standard technique in computer graphics and biomechanics. Animal locomotion has been recorded in different environments and this has been successfully used in animal biomechanical experiments. There is an increasing interest to acquire and analyze animal motion data. In this project we focus on the development of generic models for quadrupedal motion, with special respect to the movement of the spine.
In this project we work on the analysis, synthesis and resynthesis of optical material properties of cloth. By estimating domain specific parameters like the weaving pattern and yarn reflection properties from images we obtain a cloth model which can both be visually resynthesized and intuitively edited. We develop new techniques in the context of physically based rendering and image analysis of cloth.
To correctly simulate materials under arbitrary illumination, the light simulation in a virtual scene must be calculated on a pure spectral basis. This is already done in modern rendering systems. For a few classes of materials spectral reflectance data is already acquired for a few light and view directions using spectrometers and gonioreflectometer setups. This is sometimes enough to fit analytical models to the measured data. But for anisotropic materials or for materials with strong variations in angular or spatial domain there are currently no measurement setups at hand. Similar setups like the ones based on RGB CCD cameras are impractical for spectral measurements because of the high costs of cameras and light sources needed for spectral measurements. In this project we plan to combine RGB and spectral measurement methods to come up with an efficient and pratical measurement setup for spectral BTFs. Furthermore, algorithm for analysis, compression and efficient rendering for such RGB-spectral-combined data will be investigated.

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