We address the problem of segmenting 3D scan data into objects or object classes. Our segmentation framework is based on a subclass of Markov random fields (MRFs) which support efficient graph-cut inference. The MRF models incorporate a large set of diverse features and enforce the preference that adjacent scan points have the same classification label. We use a recently proposed maximum-margin framework to discriminatively train the model from a set of labeled scans; as a result we automatically learn the relative importance of the features for the segmentation task. Performing graph-cut inference in the trained MRF can then be used to segment new scenes very efficiently. We test our approach on three large-scale datasets produced by different kinds of 3D sensors, showing its applicability to both outdoor and indoor environments containing diverse objects.


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    Title :

    Discriminative learning of Markov random fields for segmentation of 3D scan data


    Contributors:
    Anguelov, D. (author) / Taskarf, B. (author) / Chatalbashev, V. (author) / Koller, D. (author) / Gupta, D. (author) / Heitz, G. (author) / Ng, A. (author)


    Publication date :

    2005-01-01


    Size :

    1485441 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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