Line Clustering and Contour Extraction in the Context of 2D Building Plans

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1 Citation (Scopus)


For the purpose of analyzing a building according to its accessibility or structural resilience, printed 2D floor plans are not sufficient because of the missing link to semantic information.
This paper tackles this issue and introduces a concept for clustering classified lines of a floor plan and for creating semantically enriched contour elements based on different image processing, computer vision and machine learning algorithms. Based on a general line clustering approach, we introduce type specific methods for walls, windows, doors and stairs. The resulting clusters are in turn used for a contour creation, which uses minimal rotated rectangles. Those rectangles are transformed to polygons that are refined using post processing steps.
The approach is evaluated via positive testing using a pixel-based comparison of the process's result. For this, automatically generated as well as real world building plans are used. The final evaluation shows, that the concept reaches a confidence of >90% for door, stair and windows and only around 10% for stairs with the run-time linearly scaling with the size of the input.
Original languageEnglish
Title of host publication29th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG 2021 - Proceedings
EditorsVaclav Skala
Number of pages10
ISBN (Electronic)9788086943343
Publication statusPublished - May 2021


  • Building Plan
  • Clustering
  • Contour Extraction
  • Image Processing
  • Machine Learning


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