%0 Conference Paper %1 Anguelov2005 %A Anguelov, D. %A Taskar, B. %A Chatalbashev, V. %A Koller, D. %A Gupta, D. %A Heitz, G. %A Ng, A. %B Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) %D 2005 %I IEEE Computer Society %K Learning Laserscanning segmentation 3DPointCloud %P 169--176 %T Discriminative learning of markov random fields for segmentation of 3d scan data %V 2 %X 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 maximummargin 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.

@inproceedings{Anguelov2005, abstract = {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 maximummargin 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.}, added-at = {2010-12-09T16:35:40.000+0100}, author = {Anguelov, D. and Taskar, B. and Chatalbashev, V. and Koller, D. and Gupta, D. and Heitz, G. and Ng, A.}, biburl = {https://www.bibsonomy.org/bibtex/262aa4351944f54249251815cd4af530f/ipi_jn}, booktitle = {Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)}, file = {:E\:\\Literatursammlung\\Proceedings\\Sonstige\\Anguelov et al (2005) - Discriminative learning of markov random fields for segmentation of 3d scan data.pdf:PDF}, interhash = {d7703ff1a719fe7460f838d6fa4f0fbb}, intrahash = {62aa4351944f54249251815cd4af530f}, issn = {1063-6919}, keywords = {Learning Laserscanning segmentation 3DPointCloud}, pages = {169--176}, publisher = {IEEE Computer Society}, timestamp = {2010-12-09T16:35:40.000+0100}, title = {Discriminative learning of markov random fields for segmentation of 3d scan data}, volume = 2, year = 2005 }