Publikation

iRep3D: Efficient Semantic 3D Scene Retrieval

Xiaoqi Cao, Matthias Klusch

In: Proceedings of the 8th International Conference on Computer Vision Theory and Applications. International Conference on Computer Vision Theory and Applications (VISAPP-13) February 21-24 Barcelona Spain ScitePress 2013.

Abstrakt

In this paper, we present a new repository, called iRep3D, for efficient retrieval of semantically annotated 3D scenes in XML3D, X3D or COLLADA. The semantics of a 3D scene can be described by means of its annotations with concepts and services which are defined in appropriate OWL2 ontologies. The iRep3D repository indexes annotated scenes with respect to these annotations and geometric features in three different scene indices. For concept and service-based scene indexing iRep3D utilizes a new approximated logical subsumption-based measure while the geometric feature-based indexing adheres to the standard specifications of XML-based 3D scene graph models. Each query for 3D scenes is processed by iRep3D in these indices in parallel and answered with the top-k relevant scenes of the final aggregation of the resulting rank lists. Results of experimental performance evaluation over a preliminary test collection of more than 600 X3D and XML3D scenes shows that iRep3D can significantly outperform both semantic-driven multimedia retrieval systems FB3D and RIR, as well as the non-semantic-based 3D model repository ADL in terms of precision and with reasonable response time in average.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence