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3D Modelling Approach for Ancient Floor Plans’ Quick Browsing

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Document Analysis Systems (DAS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13237))

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Abstract

Although 2D architectural floor plans are a commonly used way to express the design of a building, 3D models provide precious insight into modern building usability and safety. In addition, for a historical monument like the Palace of Versailles, the 3D models of its ancient floor plans help us to reconstruct the evolution of its buildings over the years. Such old floor plans are hand made and thus present some problems in automatic creation of a 3D model due to the drawing style variability. In this paper, we introduce a fully automatic and fast method to compute 3D building models from a set of architectural floor plans of the Palace of Versailles dated to the \(17^{th}\) and \(18^{th}\) century. First, we detect and localise walls in an input floor plan image using a statistical image segmentation model based on the U-net convolutional neural network architecture and a binary wall mask image is obtained. Secondly, using the generated wall mask image, the 3D model is built upon the linear edge segments representing the detected wall sides in the mask image. In order to cope with the lack of accurate ground truth information for the 3D models of ancient floor plans, we use a dedicated semi-automatic software to build a set of reference 3D models that describe plans’ wall projections from three sides of view. We evaluate the performance of our approach on an input floor plan image by measuring the overlapping between the 3D reference model and our 3D model. Our fast and fully automatic approach performs efficiently and produces quite accurate 3D models with \(84.2\%\) of IoU score in average. Furthermore, its performance surpasses the performance of the state of the art approach in the wall detection task.

First and second authors contributed equally to this research.

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Notes

  1. 1.

    https://www.etis-lab.fr/versailles-fp/.

  2. 2.

    https://verspera.hypotheses.org/.

References

  1. Ahmed, S., Liwicki, M., Weber, M., Dengel, A.: Automatic room detection and room labeling from architectural floor plans. In: 2012 10th IAPR DAS, pp. 339–343 (2012)

    Google Scholar 

  2. Ahmed, S., Weber, M., Liwicki, M., Langenhan, C., Dengel, A., Petzold, F.: Automatic analysis and sketch-based retrieval of architectural floor plans. Pattern Recognit. Lett. 35, 91–100 (2014). Frontiers in Handwriting Processing

    Article  Google Scholar 

  3. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI–8(6), 679–698 (1986)

    Article  Google Scholar 

  4. Dodge, S., Xu, J., Stenger, B.: Parsing floor plan images. In: MVA, May 2017

    Google Scholar 

  5. Dosch, P., Tombre, K., Ah-Soon, C., Masini, G.: A complete system for the analysis of architectural drawings. Int. J. Doc. Anal. Recogn. 3, 102–116 (2000)

    Article  Google Scholar 

  6. Fiorucci, M., Khoroshiltseva, M., Pontil, M., Traviglia, A., Del Bue, A., James, S.: Machine learning for cultural heritage: a survey. Pattern Recogn. Lett. 133, 102–108 (2020)

    Article  Google Scholar 

  7. Galambos, C., Kittler, J., Matas, J.: Progressive probabilistic hough transform for line detection. In: Proceedings of 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), vol. 1, pp. 554–560 (1999)

    Google Scholar 

  8. Gao, H., Yuan, H., Wang, Z., Ji, S.: Pixel deconvolutional networks. arXiv preprint arXiv:1705.06820 (2017)

  9. Grilli, E., Özdemir, E., Remondino, F.: Application of machine and deep learning strategies for the classification of heritage point clouds. Int. Arch. Photogr. Remote Sens. Spat. Inf. Sci. XLII-4/W18, 447–454 (2019)

    Google Scholar 

  10. Grilli, E., Menna, F., Remondino, F.: A review of point clouds segmentation and classification algorithms. Int. Arch. Photogr. Remote Sens. Spat. Inf. Sci. 42, 339 (2017)

    Article  Google Scholar 

  11. Guo, T., Zhang, H., Wen, Y.: An improved example-driven symbol recognition approach in engineering drawings. Comput. Graph. 36(7), 835–845 (2012). Augmented Reality Computer Graphics in China

    Article  Google Scholar 

  12. de las Heras, L.-P., Terrades, O.R., Robles, S., Sánchez, G.: CVC-FP and SGT: a new database for structural floor plan analysis and its groundtruthing tool. Int. J. Doc. Anal. Recognit. (IJDAR) 18(1), 15–30 (2015)

    Article  Google Scholar 

  13. Lampropoulos, G., Keramopoulos, E., Diamantaras, K.: Enhancing the functionality of augmented reality using deep learning, semantic web and knowledge graphs: a review. Vis. Inform. 4(1), 32–42 (2020)

    Article  Google Scholar 

  14. Liu, C., Wu, J., Kohli, P., Furukawa, Y.: Raster-to-vector: revisiting floorplan transformation. In: Proceedings of the IEEE ICCV, pp. 2195–2203 (2017)

    Google Scholar 

  15. Macé, S., Locteau, H., Valveny, E., Tabbone, S.: A system to detect rooms in architectural floor plan images. In: Proceedings of the 9th IAPR DAS, pp. 167–174 (2010)

    Google Scholar 

  16. Milletari, F., Navab, N., Ahmadi, S., V-net: fully convolutional neural networks for volumetric medical image segmentation. In: Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571 (2016)

    Google Scholar 

  17. Oliveira, S.A., Seguin, B., Kaplan, F.: dhSegment: a generic deep-learning approach for document segmentation. CoRR abs/1804.10371 (2018)

    Google Scholar 

  18. Or, S., Wong, K., Yu, Y., Chang, M.: Highly automatic approach to architectural floorplan image understanding & model generation. In: Vision, Modeling & Visualization 2005, pp. 25–32. Erlangen, Germany (2005)

    Google Scholar 

  19. Sharma, D., Gupta, N., Chattopadhyay, C., Mehta, S.: DANIEL: a deep architecture for automatic analysis and retrieval of building floor plans. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 01, pp. 420–425 (2017)

    Google Scholar 

  20. Swaileh, W., Kotzinos, D., Ghosh, S., Jordan, M., Vu, N.-S., Qian, Y.: Versailles-FP dataset: wall detection in ancient floor plans. In: Lladós, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12821, pp. 34–49. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86549-8_3

    Chapter  Google Scholar 

  21. Tabia, H., Riedinger, C., Jordan, M.: Automatic reconstruction of heritage monuments from old architecture documents. J. Electron. Imaging 26(1), 011006 (2016)

    Article  Google Scholar 

  22. Yang, S.T., Wang, F.E., Peng, C.H., Wonka, P., Sun, M., Chu, H.K.: DuLa-net: a dual-projection network for estimating room layouts from a single RGB panorama. In: Proceedings of the IEEE/CVF Conference on CVPR, pp. 3363–3372 (2019)

    Google Scholar 

  23. Yin, X., Wonka, P., Razdan, A.: Generating 3D building models from architectural drawings: a survey. IEEE Comput. Graph. Appl. 29(1), 20–30 (2008)

    Article  Google Scholar 

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Acknowledgements

We thank our colleagues of the VERSPERA research project in the Research Center of Château de Versailles, French national Archives and French national Library, and the Fondation des sciences du patrimoine which supports VERSPERA.

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Correspondence to Wassim Swaileh .

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Swaileh, W., Jordan, M., Kotzinos, D. (2022). 3D Modelling Approach for Ancient Floor Plans’ Quick Browsing. In: Uchida, S., Barney, E., Eglin, V. (eds) Document Analysis Systems. DAS 2022. Lecture Notes in Computer Science, vol 13237. Springer, Cham. https://doi.org/10.1007/978-3-031-06555-2_42

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  • DOI: https://doi.org/10.1007/978-3-031-06555-2_42

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