Neural networks have become state-of-the-art computer vision tools for tasks that learn implicit representations of geometrical scenes. This paper proposes a two-part network architecture that exploits a view-synthesis network to understand a context scene and a graph convolutional network to generate a shape body model of an object within the field of view of a spacecraft’s optical navigation sensors. Once the first part of the network’s architecture understands the spacecraft’s environment, it can generate images from novel observations. The second part uses a multiview set of images to construct a 3D graph-based representation of the object. The proposed network pipeline produces shape models with accuracies that compete with state-of-the-art methods currently used for missions to small bodies. The network pipeline can be trained for multi-environment missions. Moreover, the onboard implementation may be more cost-effective than the current state-of-the-art.


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    Titel :

    Online Shape Modeling of Resident Space Objects Through Implicit Scene Understanding


    Beteiligte:
    Heintz, Aneesh M. (Autor:in) / Peck, Mason (Autor:in) / Sun, Fangchen (Autor:in) / Mackey, Ian (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.04.2022




    Medientyp :

    Aufsatz (Konferenz) , Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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