Discriminative
Learning of Markov Random Fields for Segmentation of 3D Scan Data
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D.
Anguelov, B.Taskar, V.
Chatalbashev, D. Gupta, D. Koller, G. Heitz, A. Ng [CVPR 2005]
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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. |
Paper: [2.1MB PDF]
Below you can get supplementary materials for the datasets we
experimented with in the paper.
Dataset 1: Terrain Classification
Dataset
2: Segmentation of Articulated Objects
Dataset 3: Object Segmentation for the Princeton
Benchmark
