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RGB-D cameras, which give an RGB image to- gether with depths, are becomingincreasingly popular for robotic perception. In this paper, we address the taskof detecting commonly found objects in the 3D point cloud of indoor scenesobtained from such cameras. Our method uses a graphical model that capturesvarious features and contextual relations, including the local visualappearance and shape cues, object co-occurence relationships and geometricrelationships. With a large number of object classes and relations, the model'sparsimony becomes important and we address that by using multiple types of edgepotentials. We train the model using a maximum-margin learning approach. In ourexperiments over a total of 52 3D scenes of homes and offices (composed fromabout 550 views), we get a performance of 84.06% and 73.38% in labeling officeand home scenes respectively for 17 object classes each. We also present amethod for a robot to search for an object using the learned model and thecontextual information available from the current labelings of the scene. Weapplied this algorithm successfully on a mobile robot for the task of finding12 object classes in 10 different offices and achieved a precision of 97.56%with 78.43% recall.

Contextually Guided Semantic Labeling and Search for 3D Point Clouds