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The reliable automatic visual recognition of indoor scenes with complex object constellations using only sensor data is a nontrivial problem. In order to improve the construction of an accurate semantic 3D model of an indoor scene, we exploit human-produced verbal descriptions of the relative location of pairs of objects. This requires the ability to deal with different spatial reference frames (RF) that humans use interchangeably. In German, both the intrinsic and relative RF are used frequently, which often leads to ambiguities in referential communication. We assume that there are certain regularities that help in specific contexts.
In a first experiment, we investigated how speakers of German describe spatial relationships between different pieces of furniture. This gave us important information about the distribution of the RFs used for furniture-predicate combinations, and by implication also about the preferred spatial predicate. The results of this experiment are compiled into a computational model that extracts partial orderings of spatial arrangements between furniture items from verbal descriptions.
In the implemented system, the visual scene is initially scanned by a 3D camera system. From the 3D point cloud, we extract point clusters that suggest the presence of certain furniture objects. We then integrate the partial orderings extracted from the verbal utterances incrementally and cumulatively with the estimated probabilities about the identity and location of objects in the scene, and also estimate the probable orientation of the objects.
This allows the system to significantly improve both the accuracy and richness of its visual scene representation.