This paper presents a novel method for summarizing and compression of large-scale 3D models into compact spherical representations. The information is combined into a set of optimized spheres in order to facilitate its use by systems with limited resources (smartphones, robots, UAVs, …). This vision-based summarizing process is applied in a fully automatic way using jointly photometric, geometric and semantic information of the studied environment. The main contribution of this research is to provide a navigability graph that maximizes the significance of the contents of its nodes while maintaining the full visibility of the environment. Experimental results in summarizing large-scale 3D map demonstrate the feasibility of our approach and evaluate the performance of the algorithm.


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    Titel :

    Navigability Graph Extraction From Large-Scale 3D Point Cloud


    Beteiligte:


    Erscheinungsdatum :

    2018-11-01


    Format / Umfang :

    1465763 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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