Inspired by the ideas behind superpixels, which segment an image into homogenous regions to accelerate subsequent processing steps (e.g. tracking), we present a sensor-fusion-based segmentation approach that generates dense depth regions referred to as supersurfaces. This method aggregates both a point cloud from a LiDAR and an image from a camera to provide an over-segmentation of the three-dimensional scene into piece-wise planar surfaces by utilizing a multi-label Markov Random Field (MRF). A comparison between this method that generates supersurfaces, image-based superpixels, and RGBD-based segments using a subset of KITTI dataset is provided in the experimental results. We observed that supersurfaces are less redundant and more accurate in terms of average boundary recall for a fixed number of segments.


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    Titel :

    Region segmentation using LiDAR and camera


    Beteiligte:
    Daraei, M. Hossein (Autor:in) / Vu, Anh (Autor:in) / Manduchi, Roberto (Autor:in)


    Erscheinungsdatum :

    01.10.2017


    Format / Umfang :

    1385889 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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