Lehrstuhl für Informatik 9 (Graphische Datenverarbeitung)


Since more than 25 years, the Chair of Visual Computering researches methods for the generation, processing and analysis von images and 3D-models with computers. This includes disciplines such as computer graphics, visualization, geometry processing, virtual reality, computer vision, or 3d reconstruction.

The research foci of the group are:

  • Rendering & Visualization: In rendering, algorithms are examined to synthesize images from given virtual 3D-objects or worlds. Rendering methods can be focused on the generation of realistic images (photo-realistic rendering) or the fast synthesis of the images, resulting in fluent movement (real-time rendering). In visualization, the goal is to render images that are realistic, but that gain insight to all types of data, from measured data (e.g., medical CT scans), simulated data (flow simulation data), or non-spatial, abstract data such as the spread of news in twitter.

  • Geometric Modelling & 3D-Reconstruction: In this research area, we examine methods to generate 3D-models (e.g. for later rendering or visualization), and to process and analyse these models.  This includes representations of 3D surfaces in a computer, and algorithms for the animation of these objects, or their physical simulation. In 3D-reconstruction, we reconstruct 3D-models from images or with the help of more advanced sensors, in particular depth cameras such as the Microsoft Kinect.

  • Virtual and Augmented Reality: In virtual reality a user immerges into a virtual world using special display devices. One currently popular such device are Head Mounted Displays, where a user looks around in a virtual world by turning his head or walking around while wearing the display. In augmented reality, the real environment is augmented with virtual objects. Again, this requires special display devices that overlay the real world with virtual content (e.g. AR glasses such as the Microsoft Hololens), or by using projectors that display virtual content on their surrounding.

  • Visual Computing in the Humanities and Social Sciences: In this research area, we apply and develop methods from Visual Computing to answer question from humanities and social sciences. A typical example is archaeology, where we digitize ancient artefacts using 3D-reconstruction, analyse them using approaches from geometry processing, and visualize them using virtual reality.

  • In diesem Bereich wird die Anwendung von  Verfahren des Visual Computings zur Beantwortung von Fragen der Geistes- und Sozialwissenschaften erforscht. Typischer Anwendungsbereich ist die Archäologie, in der mit Methoden des Visual Computings Artefakte dreidimensional digitalisiert und analysiert, sowie mit virtueller oder erweiterter Realität dargestellt oder in anderem Kontext visualisiert werden können. Der Lehrstuhl ist im interdisziplinären Zentrum "IZ digital" sowie dem Studiengang Digitale Geistes- und Sozialwissenschaften engagiert.

Cauerstraße 11
91058 Erlangen

Research Fields

Geometric Modeling and 3D Reconstruction
Rendering and Visualization
Virtual, Mixed, and Augmented Reality
Visual Computing for Digital Humanities and Social Sciences

Related Project(s)

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OCEAN: Rechenleistungsoptimierte Software-Strategien für auf unstrukturierten Gittern basierende Anwendungen in der Ozeanmodellierung
Vadym Aizinger; Dr. Roberto Grosso; Prof. Dr. Harald Köstler
(01/01/2017 - 30/09/2020)

Bildforensik: Analyse der digitalen Integrität von Bildern und Videos
Dr.-Ing. Christian Riess; Prof. Dr. Marc Stamminger

Metalinguistic Abstractions for Graphics Applications
Kai Selgrad; Prof. Dr. Marc Stamminger

Projection Mapping
Dr.-Ing. Frank Bauer; Prof. Dr. Marc Stamminger
(01/10/2014 - 01/10/2025)

Advanced Raytracing
Prof. Dr. Marc Stamminger

Publications (Download BibTeX)

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Innmann, M., Kim, K., Gu, J., Niessner, M., Loop, C., Stamminger, M., & Kautz, J. (2019). NRMVS: Non-Rigid Multi-View Stereo. arXiv.
Roessler, A., Cozzolino, D., Verdoliva, L., Rieß, C., Thies, J., & Niessner, M. (2019). FaceForensics++: Learning to Detect Manipulated Facial Images.
Matern, F., Riess, C., & Stamminger, M. (2019). Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations. In IEEE Workshop on Applications in Computer Vision (pp. 83-92). Waikoloa Village, HI, US: IEEE.
Herglotz, C., Müller, D., Weinlich, A., Bauer, F., Ortner, M., Stamminger, M., & Kaup, A. (2018). Improving HEVC Encoding of Rendered Video Data Using True Motion Information. In Proceedings of the 20th IEEE International Symposium on Multimedia. Taichung, TW.
Lange, V., Kurth, P., Keinert, B., Boß, M., Stamminger, M., & Bauer, F. (2018). Proxy Painting. In Sablatnig Robert, Wimmer Michael (Eds.), Eurographics Workshop on Graphics and Cultural Heritage (pp. 97-104). Wien, AT: The Eurographics Association.
Kurth, P., Lange, V., Siegl, C., Stamminger, M., & Bauer, F. (2018). Auto-Calibration for Dynamic Multi-Projection Mapping on Arbitrary Surfaces. IEEE Transactions on Visualization and Computer Graphics, 24(11), 2886-2894. https://dx.doi.org/10.1109/TVCG.2018.2868530
Lier, A., Martinek, M., Stamminger, M., & Selgrad, K. (2018). A High-Resolution Compression Scheme for Ray Tracing Subdivision Surfaces with Displacement. In Proceedings of the ACM on Computer Graphics and Interactive Techniques, Volume 1 Issue 2, August 2018. Vancouver, CA: New York, NY, USA: ACM.
Lier, A., Stamminger, M., & Selgrad, K. (2018). CPU-Style SIMD Ray Traversal on GPUs. In ACM New York, NY, USA (Eds.), HPG '18 Proceedings of the Conference on High-Performance Graphics. Vancouver, CA.
Seuffert, J., Stamminger, M., & Riess, C. (2018). Towards Forensic Exploitation of 3-D Lighting Environments in Practice. In Beiträge der 9. Jahrestagung des Fachbereichs Sicherheit der Gesellschaft fü}r Informatik e.V. (GI) (pp. 159--169). Konstanz.
Thies, J., Zollhöfer, M., Stamminger, M., Theobalt, C., & Nießner, M. (2018). FaceVR: Real-Time Facial Reenactment and Eye Gaze Control in Virtual Reality. ACM Transactions on Graphics, 37(2). https://dx.doi.org/10.1145/3182644
Eibel, C., Do, T.-N., Meißner, R., & Distler, T. (2018). Empya: Saving Energy in the Face of Varying Workloads. In Proceedings of the 6th International Conference on Cloud Engineering (IC2E '18) (pp. 134-140). Orlando, USA.
Martinek, M., Stamminger, M., Binder, N., & Keller, A. (2018). Compressed Bounding Volume Hierarchies for Effiocient Ray Tracing of Disperse Hair. In Proceedings of the VMV 2018 (pp. 97-102).
Franke, L., Hofmann, N., Stamminger, M., & Selgrad, K. (2018). Multi-Layer Depth of Field Rendering with Tiled Splatting. In ACM (Eds.), . Montreal, CA.
Thies, J., Zollhöfer, M., Stamminger, M., Theobalt, C., & Nießner, M. (2018). HeadOn: Real-time Reenactment of Human Portrait Videos. ACM Transactions on Graphics, 37(4). https://dx.doi.org/10.1145/3197517.3201350
Keinert, B., Martschinke, J., & Stamminger, M. (2018). Learning Real-Time Ambient Occlusion from Distance Representations. In ACM (Eds.), . Montreal, CA.
Zollhöfer, M., Thies, J., Garrido, P., Bradley, D., Beeler, T., Pérez, P.,... Nießner, M. (2018). State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications. Computer Graphics Forum. https://dx.doi.org/10.1111/cgf.13382
Innmann, M., Erhardt, P., Schütz, D., & Greiner, G. (2017). Automatic Transfer of Landmarks on Human Skulls using GPU-based Non-rigid Registration. In The Eurographics Association (Eds.), GCH 2017 - Eurographics Workshop on Graphics and Cultural Heritage (pp. 131 - 135). Graz, AT.
Piazza, A., Süßmuth, J., & Bodendorf, F. (2017). Body measure-aware fashion product recommendations: evaluating the predictive power of body scan data. In Toine Bogers, Marijn Koolen, Bamshad Mobasher, Alan Said, Alexander Tuzhilin (Eds.), Proceedings of the RecSys 2017 Workshop on Recommendation in Complex Scenarios co-located with 11th ACM Conference on Recommender Systems (RecSys 2017) (pp. 5-8). Como, Italy, IT: Aachen: CEUR-WS.org.
Siegl, C., Lange, V., Stamminger, M., Bauer, F., & Thies, J. (2017). FaceForge: Markerless Non-Rigid Face Multi-Projection Mapping. IEEE Transactions on Visualization and Computer Graphics, PP(99). https://dx.doi.org/10.1109/TVCG.2017.2734428
Aichinger, W., Krappe, S., Enis, A.C., Cetin-Atalay, R., Üner, A., Benz, M.,... Münzenmayer, C. (2017). Automated cancer stem cell recognition in H and E stained tissue using convolutional neural networks and color deconvolution. In Proceedings Volume 10140 Medical Imaging 2017:Digital Pathology. Orlando, Florida, USA, US.

Last updated on 2018-05-07 at 04:31