Vehicle reconstruction from single aerial images is an important but challenging task. In this work, we introduce a new framework based on convolutional neural networks (CNN) that performs monocular detection, pose, shape and type estimation for vehicles in UAV imagery, taking advantage of a strong 3D object model. In the final training phase, all components of the model are trained end-to-end. We present a UAV-based dataset for the evaluation of our model and additionally evaluate it on an augmented version of the Hessingheim benchmark dataset. Our method presents encouraging pose and shape estimation results: Based on images of 3 cm GSD, it achieves median errors of up to 5 cm in position and 3◦ in orientation, and RMS errors of ±7 cm and ±24 cm in planimetry and height, respectively, for keypoints describing the car shape.


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    Titel :

    Vehicle Pose and Shape Estimation in UAV Imagery Using a CNN


    Beteiligte:

    Erscheinungsdatum :

    2023-01-01


    Anmerkungen:

    ISPRS Geospatial Week 2023 ; ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; X-1/W1-2023



    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    550 / 629




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