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.