Efficient Retrieval of 3D Building Models Using Embeddings of Attributed Subgraphs

In proceedings of the 20th ACM Conference on Information and Knowledge Management (CIKM 2011) : Posters, Glasgow, UK, Oct. 2011
 

Abstract

We present a novel method for retrieval and classification of 3D building models that is tailored to the specific requirements of architects. In contrast to common approaches our algorithm relies on the interior spatial arrangement of rooms instead of exterior geometric shape. We first represent the internal topological building structure by a Room Connectivity Graph (RCG). To enable fast and efficient retrieval and classification with RCGs, we transform the structured graph representation into a vector-based one by introducing a new concept of subgraph embeddings. We provide comprehensive experiments showing that the introduced subgraph embeddings yield superior performance compared to state-of-the-art graph retrieval approaches.

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Bibtex

@INPROCEEDINGS{wessel-2011-EfficientPosterPaper,
        author = {Wessel, Raoul and Ochmann, Sebastian and Vock, Richard and Bl{\"u}mel, Ina and Klein, Reinhard},
         title = {Efficient Retrieval of 3D Building Models Using Embeddings of Attributed Subgraphs},
     booktitle = {the 20th ACM Conference on Information and Knowledge Management (CIKM 2011) : Posters},
          year = {2011},
         month = oct,
      location = {Glasgow, UK},
  howpublished = {to appear in Poster Paper Proceedings of ACM Conference on Information and Knowledge Management
                  (CIKM 2011)},
      abstract = {We present a novel method for retrieval and classification of 3D building models that is tailored to
                  the specific requirements of architects. In contrast to common approaches our algorithm relies on
                  the interior spatial arrangement of rooms instead of exterior geometric shape. We first represent
                  the internal topological building structure by a Room Connectivity Graph (RCG). To enable fast and
                  efficient retrieval and classification with RCGs, we transform the structured graph representation
                  into a vector-based one by introducing a new concept of subgraph embeddings. We provide
                  comprehensive experiments showing that the introduced subgraph embeddings yield superior performance
                  compared to state-of-the-art graph retrieval approaches.}
}