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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    Anguelov, D. (Autor:in) / Taskarf, B. (Autor:in) / Chatalbashev, V. (Autor:in) / Koller, D. (Autor:in) / Gupta, D. (Autor:in) / Heitz, G. (Autor:in) / Ng, A. (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    1485441 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Discriminative Random Fields

    Kumar, S. / Hebert, M. | British Library Online Contents | 2006



    Segmentation of Rumex obtusifolius using Gaussian Markov random fields

    Hiremath, S. | British Library Online Contents | 2013


    Human Action Segmentation and Recognition Using Discriminative Semi-Markov Models

    Shi, Q. / Cheng, L. / Wang, L. et al. | British Library Online Contents | 2011