Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data

Authors:
Dragomir Anguelov;Ben Taskar;Vassil Chatalbashev;Daphne Koller;Dinkar Gupta;Geremy Heitz;Andrew Ng
Affiliations:
Stanford University;University of California at Berkeley;Stanford University;Stanford University;Stanford University;Stanford University;Stanford University
Venue:
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Year:
2005

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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 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.