We present an improved model for MRF-based depth upsampling, guided by image-as well as 3D surface normal features. By exploiting the underlying camera model we define a novel regularization term that implicitly evaluates the planarity of arbitrary oriented surfaces. Our method improves upsampling quality in scenes composed of predominantly planar surfaces, such as urban areas. We use a synthetic dataset to demonstrate that our approach outperforms recent methods that implement distance-based regularization terms. Finally, we validate our approach for mapping applications on our experimental vehicle.


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    Title :

    Guided depth upsampling for precise mapping of urban environments


    Contributors:


    Publication date :

    2017-06-01


    Size :

    1684904 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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