The Institute for Computer Graphics and Knowledge Visualization participates since 2012 in the joint PhD programme, with a focus on research in Geometric Modeling, Visualization and Novel Applications in Visual Computing. We here describe our ongoing and future work. For more information, please contact the Principal Investigators:
For more general inforamtion on the dual degree program, please see here.
Because modeling via code can often by quite tedious, simplifications for various modeling domains are of great help. One example is shape grammars. They are formal grammars (often context-free) that generate geometry. For regular and hierarchical structures, like building facades, they provide a much easier way of modeling compared to other procedural techniques. Therefore, finding new domain specific ways for describing 3D models or further developing existing ones, will increase the potential of procedural modeling. This project looks at application areas like roof modeling and advanced shape grammars systems.
The goal of the project is to make it easier to produce good coarse 3D meshes from densely scanned data. We have methods for finding curvatures and features in a point cloud or triangle mesh and, based on those, developed a software tool which aids the manual generation of good subdivision control meshes from scan data. We want to investigate methods to automatically reduce a point cloud or triangle mesh to a good coarse subdivision base mesh. A good subdivision mesh is not only accurate and coarse but also regular. The method for finding high quality quadrilateral representations should be general and applicable to complex meshes of arbitrary topology.
Due to recent advances in 3D acquisition and modeling, increasingly large amounts of 3D shape data become available in many application domains. Coping with large 3D shape data requires effective and efficient techniques to support 3D searching, exploring, analyzing and processing tasks. Our research group works on feature-based approaches for 3D shape retrieval including sketch- and example-based methods; clustering and classification of shape repositories for annotation and visual exploration; and on similarity-based methods for restoration of fragmented or incomplete shapes. In this project, we want to evaluate novel feature-based shape analysis methods to support one or more of the above mentioned problems. The methods to be researched are informed by new application needs in domains such as Computer Aided Design and Digital Archeology.