Abstract
In this work, we propose a deep neural network, FPNet, for parsing and recognizing floor plan elements. We develop a multi-task deep attention network to recognize room boundaries and room types in CAD floor plans. We evaluate our network on multiple datasets. We perform quantitative analysis along three metrics - Overall accuracy, Mean accuracy, and Intersection over union (IoU) to evaluate the efficacy of our approach. We compare our approach with the existing baseline and significantly outperform on all these metrics.
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References
Ah-Soon, C., Tombre, K.: Variations on the analysis of architectural drawings. In: Proceedings of the Fourth International Conference on Document Analysis and Recognition, vol. 1, pp. 347–351. IEEE (1997)
Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801–818 (2018)
Dodge, S., Xu, J., Stenger, B.: Parsing floor plan images. In: 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA), pp. 358–361. IEEE (2017)
Dosch, P., Tombre, K., Ah-Soon, C., Masini, G.: A complete system for the analysis of architectural drawings. Int. J. Doc. Anal. Recognit. 3(2), 102–116 (2000)
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)
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)
de las Heras, L.P., Terrades, O., Robles, S., S’anchez, G.: CVC-FP and SGT: a new database for structural floor plan analysis and its groundtruthing tool. Int. J. Doc. Anal. Recognit. (2015)
Kalervo, A., Ylioinas, J., Häikiö, M., Karhu, A., Kannala, J.: CubiCasa5K: a dataset and an improved multi-task model for floorplan image analysis. In: Felsberg, M., Forssén, P.-E., Sintorn, I.-M., Unger, J. (eds.) SCIA 2019. LNCS, vol. 11482, pp. 28–40. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20205-7_3
Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7482–7491 (2018)
Liu, C., Wu, J., Kohli, P., Furukawa, Y.: Raster-to-vector: revisiting floorplan transformation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2195–2203 (2017)
Liu, C., Schwing, A.G., Kundu, K., Urtasun, R., Fidler, S.: Rent3D: floor-plan priors for monocular layout estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3413–3421 (2015)
Lv, X., Zhao, S., Yu, X., Zhao, B.: Residential floor plan recognition and reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16717–16726 (2021)
Oktay, O., et al.: Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Ryall, K., Shieber, S., Marks, J., Mazer, M.: Semi-automatic delineation of regions in floor plans. In: Proceedings of 3rd International Conference on Document Analysis and Recognition, vol. 2, pp. 964–969. IEEE (1995)
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. 1, pp. 420–425. IEEE (2017)
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)
Yamasaki, T., Zhang, J., Takada, Y.: Apartment structure estimation using fully convolutional networks and graph model. In: Proceedings of the 2018 ACM Workshop on Multimedia for Real Estate Tech, pp. 1–6 (2018)
Zeng, Z., Li, X., Yu, Y.K., Fu, C.W.: Deep floor plan recognition using a multi-task network with room-boundary-guided attention. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9096–9104 (2019)
Zhang, Y., He, Y., Zhu, S., Di, X.: The direction-aware, learnable, additive kernels and the adversarial network for deep floor plan recognition. arXiv preprint arXiv:2001.11194 (2020)
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881–2890 (2017)
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Upadhyay, A., Dubey, A., Kuriakose, S.M. (2023). FPNet: Deep Attention Network for Automated Floor Plan Analysis. In: Coustaty, M., Fornés, A. (eds) Document Analysis and Recognition – ICDAR 2023 Workshops. ICDAR 2023. Lecture Notes in Computer Science, vol 14193. Springer, Cham. https://doi.org/10.1007/978-3-031-41498-5_12
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