  RGB-D cameras, which give an RGB image to- gether with depths, are becoming
increasingly popular for robotic perception. In this paper, we address the task
of detecting commonly found objects in the 3D point cloud of indoor scenes
obtained from such cameras. Our method uses a graphical model that captures
various features and contextual relations, including the local visual
appearance and shape cues, object co-occurence relationships and geometric
relationships. With a large number of object classes and relations, the model's
parsimony becomes important and we address that by using multiple types of edge
potentials. We train the model using a maximum-margin learning approach. In our
experiments over a total of 52 3D scenes of homes and offices (composed from
about 550 views), we get a performance of 84.06% and 73.38% in labeling office
and home scenes respectively for 17 object classes each. We also present a
method for a robot to search for an object using the learned model and the
contextual information available from the current labelings of the scene. We
applied this algorithm successfully on a mobile robot for the task of finding
12 object classes in 10 different offices and achieved a precision of 97.56%
with 78.43% recall.
