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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    Title :

    Region segmentation using LiDAR and camera


    Contributors:


    Publication date :

    2017-10-01


    Size :

    1385889 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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