Welcome to the Computer Graphics Group at RWTH Aachen University!

The research and teaching activities at our institute focus on geometry acquisition and processing, on interactive visualization, and on related areas such as computer vision, photo-realistic image synthesis, and ultra high speed multimedia data transmission.

In our projects we are cooperating with various industry companies as well as with academic research groups around the world. Results are published and presented at high-profile conferences and symposia. Additional funding sources, among others, are the Deutsche Forschungsgemeinschaft and the European Union.

A video from Prof. Leif Kobbelt's talk at the Future Lab Gala in the Theater Aachen on May 11, 2016 is available online here.

June 14, 2016

We have a paper on Scale-Invariant Directional Alignment of Surface Parametrizations at the Eurographics Symposium on Geometry Processing 2016.

June 8, 2016

We have a paper on Identifying Style of 3D Shapes using Deep Metric Learning at the Eurographics Symposium on Geometry Processing 2016.

June 1, 2016

Prof. Dr. Leif Kobbelt gave a talk at the FutureLab Gala in Aachen.

Prof. Dr. Leif Kobbelt gave a talk at the FutureLab Gala in Aachen on why understanding the Pythagorean Theorem is „enough" to run the entire reconstruction pipeline for highly detailed textured 3D models of real objects and scenes.

May 11, 2016

Prof. Dr. Leif Kobbelt received his certificate of appointment from the North Rhine-Westphalian Academy of Sciences, Humanities and the Arts at the annual ceremony in Düsseldorf.


May 11, 2016

Dr. Marcel Campen received the Eurographics PhD Award 2016 at Eurographics in Lisbon. (Link)

May 9, 2016

Recent Publications

Non-Linear Shape Optimization Using Local Subspace Projections


In this paper we present a novel method for non-linear shape opti- mization of 3d objects given by their surface representation. Our method takes advantage of the fact that various shape properties of interest give rise to underdetermined design spaces implying the existence of many good solutions. Our algorithm exploits this by performing iterative projections of the problem to local subspaces where it can be solved much more efficiently using standard numer- ical routines. We demonstrate how this approach can be utilized for various shape optimization tasks using different shape parameteri- zations. In particular, we show how to efficiently optimize natural frequencies, mass properties, as well as the structural yield strength of a solid body. Our method is flexible, easy to implement, and very fast.


HexEx: Robust Hexahedral Mesh Extraction


State-of-the-art hex meshing algorithms consist of three steps: Frame-field design, parametrization generation, and mesh extraction. However, while the first two steps are usually discussed in detail, the last step is often not well studied. In this paper, we fully concentrate on reliable mesh extraction. Parametrization methods employ computationally expensive countermeasures to avoid mapping input tetrahedra to degenerate or flipped tetrahedra in the parameter domain because such a parametrization does not define a proper hexahedral mesh. Nevertheless, there is no known technique that can guarantee the complete absence of such artifacts. We tackle this problem from the other side by developing a mesh extraction algorithm which is extremely robust against typical imperfections in the parametrization. First, a sanitization process cleans up numerical inconsistencies of the parameter values caused by limited precision solvers and floating-point number representation. On the sanitized parametrization, we extract vertices and so-called darts based on intersections of the integer grid with the parametric image of the tetrahedral mesh. The darts are reliably interconnected by tracing within the parametrization and thus define the topology of the hexahedral mesh. In a postprocessing step, we let certain pairs of darts cancel each other, counteracting the effect of flipped regions of the parametrization. With this strategy, our algorithm is able to robustly extract hexahedral meshes from imperfect parametrizations which previously would have been considered defective. The algorithm will be published as an open source library.


Directional Field Synthesis, Design, and Processing


Direction fields and vector fields play an increasingly important role in computer graphics and geometry processing. The synthesis of directional fields on surfaces, or other spatial domains, is a fundamental step in numerous applications, such as mesh generation, deformation, texture mapping, and many more. The wide range of applications resulted in definitions for many types of directional fields: from vector and tensor fields, over line and cross fields, to frame and vector-set fields. Depending on the application at hand, researchers have used various notions of objectives and constraints to synthesize such fields. These notions are defined in terms of fairness, feature alignment, symmetry, or field topology, to mention just a few. To facilitate these objectives, various representations, discretizations, and optimization strategies have been developed. These choices come with varying strengths and weaknesses. This report provides a systematic overview of directional field synthesis for graphics applications, the challenges it poses, and the methods developed in recent years to address these challenges.

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