Professor Dr.

Reinhard Klein

Head of Computer Graphics Group
 
Friedrich-Hirzebruch-Allee 8, Room
D-53115 Bonn
Germany
3.016
Phone: +49 (0) 228 73-4201
Fax: +49 (0) 228 73-4212
Email: rk@REMOVETHISPART.cs.uni-bonn.de

Reinhard Klein studied Mathematics and Physics at the externUniversity of Tübingen, Germany, from where he received his MS in Mathematics (Dipl.-Math.) in 1989 and his PhD in computer science in 1995. In 1999 he received an appointment as lecturer ("Habilitation") in computer science also from the University of Tübingen, with a thesis in computer graphics. In September 1999 he became an Associate Professor at the externUniversity of Darmstadt, Germany and head of the research group externAnimation and Image Communication at the externFraunhofer Institute for Computer Graphics. Since October 2000 he is professor at the University of Bonn and director of the Institute of Computer Science II.

List of publications

List of supervised dissertations

Courses

Ongoing Projects

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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.
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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.
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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.
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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.
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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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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.
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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.

Completed Projects