Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Creating generative models from range images
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Topology matching for fully automatic similarity estimation of 3D shapes
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Journal of Global Optimization
Generative versus Discriminative Methods for Object Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Partial and approximate symmetry detection for 3D geometry
ACM SIGGRAPH 2006 Papers
Procedural modeling of buildings
ACM SIGGRAPH 2006 Papers
ACM SIGGRAPH 2007 papers
Discovering structural regularity in 3D geometry
ACM SIGGRAPH 2008 papers
Compilation of procedural models
Web3D '08 Proceedings of the 13th international symposium on 3D web technology
Introduction to shape grammars
ACM SIGGRAPH 2008 classes
Semantic fitting and reconstruction
Journal on Computing and Cultural Heritage (JOCCH)
Guest Editors' Introduction: 3D Documents
IEEE Computer Graphics and Applications
Content-Based 3D Object Retrieval
IEEE Computer Graphics and Applications
A connection between partial symmetry and inverse procedural modeling
ACM SIGGRAPH 2010 papers
Modeling procedural knowledge: a generative modeler for cultural heritage
EuroMed'10 Proceedings of the Third international conference on Digital heritage
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''What is the difference between a cup and a door?'' These kinds of questions have to be answered in the context of digital libraries. This semantic information, which describes an object on a high, abstract level, is needed in order to provide digital library services such as indexing, markup and retrieval. In this paper we present a new approach to encode and to extract such semantic information. We use generative modeling techniques to describe a class of objects: each class is represented by one algorithm; and each object is one set of high-level parameters, which reproduces the object if passed to the algorithm. Furthermore, the algorithm is annotated with semantic information, i.e. a human-readable description of the object class it represents. We use such an object description to recognize objects in real-world data e.g. laser scans. Using an algorithmic object description, we are able to identify 3D subparts, which can be described and generated by the algorithm. Furthermore, we can determine the needed input parameters. In this way, we can classify objects, recognize them semantically and we can determine their parameters (cup's height, radius, etc.).