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This work describes the automatic segmentation of 2-dimensional indoor maps into semantic units along lines of spatial function, such as connectivity or objects used for certain tasks. Using a conceptually simple and readily extensible energy maximization framework, segmentations similar to what a human might produce are demonstrated on several real-world datasets. In addition, it is shown how the system can perform reference resolution by adding corresponding potentials to the energy function, yielding a segmentation that responds to the context of the spatial reference.

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