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Statistical segmentation and structural recognition for floor plan interpretation

Notation invariant structural element recognition

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Abstract

A generic method for floor plan analysis and interpretation is presented in this article. The method, which is mainly inspired by the way engineers draw and interpret floor plans, applies two recognition steps in a bottom-up manner. First, basic building blocks, i.e., walls, doors, and windows are detected using a statistical patch-based segmentation approach. Second, a graph is generated, and structural pattern recognition techniques are applied to further locate the main entities, i.e., rooms of the building. The proposed approach is able to analyze any type of floor plan regardless of the notation used. We have evaluated our method on different publicly available datasets of real architectural floor plans with different notations. The overall detection and recognition accuracy is about 95 %, which is significantly better than any other state-of-the-art method. Our approach is generic enough such that it could be easily adopted to the recognition and interpretation of any other printed machine-generated structured documents.

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Notes

  1. In Germany, a DIN-standard exists (DIN 1356-1), but is rarely used. Furthermore, standards vary from country to country and often even from one architecture company to another. Depending on the visual appealing, the architects within the same office decide to use different representation.

  2. http://jgrapht.org/.

  3. In the rest of this section, all the process explained for door detection is also valid for window detection. However, we will only refer to doors for clarity and to avoid unnecessary repetitions.

  4. http://www.cvc.uab.es/floorplans.

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Acknowledgments

This work has been partially supported by the Spanish projects TIN2009-14633-C03-03 and TIN2011-24631, and by the research grant of the Universitat Autònoma de Barcelona (471-02-1/2010).

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Authors and Affiliations

  1. Computer Vision Center, Barcelona, Spain

    Lluís-Pere de las Heras, Ernest Valveny & Gemma Sánchez

  2. German Research Center for AI (DFKI), Kaiserslautern, Germany

    Sheraz Ahmed & Marcus Liwicki

Authors
  1. Lluís-Pere de las Heras
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  2. Sheraz Ahmed
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  3. Marcus Liwicki
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  4. Ernest Valveny
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  5. Gemma Sánchez
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Correspondence to Lluís-Pere de las Heras.

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de las Heras, LP., Ahmed, S., Liwicki, M. et al. Statistical segmentation and structural recognition for floor plan interpretation. IJDAR 17, 221–237 (2014). https://doi.org/10.1007/s10032-013-0215-2

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  • DOI: https://doi.org/10.1007/s10032-013-0215-2

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