BIM Style Restoration Based on Image Retrieval and Object Location Using Convolutional Neural Network
Abstract
:1. Introduction
2. Related Work
3. Method
3.1. AM-rVGG Network Model
3.2. Texture Map Restore
4. Experimental Results and Analysis
4.1. Dataset
4.2. Experimental Setup
4.3. Experimental Results and Analysis
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Zhang, J.; Long, Y.; Lv, S.; Xiang, Y. BIM-enabled Modular and Industrialized Construction in China. Procedia Eng. 2016, 145, 1456–1461. [Google Scholar] [CrossRef] [Green Version]
- Zheng, Z.; Liao, W.; Lin, J.; Zhou, Y.; Zhang, C.; Lu, X. Digital Twin-Based Investigation of a Building Collapse Accident. Adv. Civ. Eng. 2022, 2022, 9568967. [Google Scholar] [CrossRef]
- Hull, J.; Ewart, I.J. Conservation data parameters for BIM-enabled heritage asset management. Autom. Constr. 2020, 119, 103333. [Google Scholar] [CrossRef]
- Adamopoulos, E.; Rinaudo, F. Close-Range Sensing and Data Fusion for Built Heritage Inspection and Monitoring—A Review. Remote Sens. 2021, 13, 3936. [Google Scholar] [CrossRef]
- Cogima, C.K.; Paiva, P.V.V.; Dezen-Kempter, E.; Carvalho, M.A.G.; Soibelman, L. The role of knowledge-based information on BIM for built heritage. In Advances in Informatics and Computing in Civil and Construction Engineering; Springer: Cham, Switzerland, 2019; pp. 27–34. [Google Scholar]
- Gunn, T.G. The mechanization of design and manufacturing. Sci. Am. 1982, 247, 114–131. [Google Scholar] [CrossRef]
- Machete, R.; Falcão, A.P.; Gonçalves, A.B.; Godinho, M.; Bento, R. Development of a Manueline style object library for heritage BIM. Int. J. Archit. Herit. 2021, 15, 1930–1941. [Google Scholar] [CrossRef]
- Qiu, Q.; Zhou, X.; Zhao, J.; Yang, Y.; Tian, S.; Wang, J.; Liu, J.; Liu, H. From sketch BIM to design BIM: An element identification approach using Industry Foundation Classes and object recognition. Build. Environ. 2021, 188, 107423. [Google Scholar] [CrossRef]
- Nie, W.; Zhao, Y.; Nie, J.; Liu, A.A.; Zhao, S. CLN: Cross-Domain Learning Network for 2D Image-Based 3D Shape Retrieval. IEEE Trans. Circuits Syst. Video Technol. 2021, 32, 992–1005. [Google Scholar] [CrossRef]
- Krizhevsky, A.; Sutskever, I.; Hinton, G. ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 2012, 25, 1097–1105. [Google Scholar] [CrossRef] [Green Version]
- Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv 2014, arXiv:1409.1556. [Google Scholar]
- He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016; pp. 770–778. [Google Scholar]
- Kim, J.; Song, J.; Lee, J.K. Recognizing and classifying unknown object in BIM using 2D CNN. In Proceedings of the International Conference on Computer-Aided Architectural Design Futures, Daejeon, Republic of Korea, 26–28 June 2019; pp. 47–57. [Google Scholar]
- Wan-qi, W.A.N.G.; Bao-rui, M.A.; Qian, L.I.; Wen-long, L.U.; Yu-shen, L.I.U. Clustering of BIM components based on similarity measurement of attributes. J. Graph. 2020, 41, 304. [Google Scholar]
- Wang, J.; Su, D.; Zhou, X. BIM model similarity calculation method. J. Graph. 2020, 41, 624–631. [Google Scholar]
- Dautov, E.; Astafeva, N. Convolutional neural network in the classification of architectural styles of buildings. In Proceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus), St. Petersburg, Moscow, Russia, 26–28 January 2021; pp. 274–277. [Google Scholar]
- Zhao, P.; Miao, Q.; Song, J.; Qi, Y.; Liu, R.; Ge, D. Architectural style classification based on feature extraction module. IEEE Access 2018, 6, 52598–52606. [Google Scholar] [CrossRef]
- Xia, B.; Li, X.; Shi, H.; Chen, S.; Chen, J. Style classification and prediction of residential buildings based on machine learning. J. Asian Archit. Build. Eng. 2020, 19, 714–730. [Google Scholar] [CrossRef]
- Bermeitinger, B.; Freitas, A.; Donig, S.; Handschuh, S. Object classification in images of Neoclassical furniture using Deep Learning. Int. Workshop Comput. Hist. Data-Driven Humanit. 2016, 2016, 109–112. [Google Scholar]
- Hu, Z.; Wen, Y.; Liu, L.; Jiang, J.; Hong, R.; Wang, M.; Yan, S. Visual classification of furniture styles. ACM Trans. Intell. Syst. Technol. (TIST) 2017, 8, 1–20. [Google Scholar] [CrossRef]
- Hu, W. The experiment of neural network on the cognition of style. In Proceedings of the 26th CAADRIA Conference, Hong Kong, China, 29 March–1 April 2021; pp. 61–70. [Google Scholar]
- Du, X. FISC: Furniture image style classification model based on Gram transformation. In Proceedings of the 2021 3rd International Conference on Advanced Information Science and System (AISS 2021), Sanya, China, 26–28 November 2021; pp. 1–5. [Google Scholar]
- Wang, Y.; Gao, W.; Wang, Y. Application of furniture images selection based on neural network. In AIP Conference Proceedings; AIP Publishing LLC: Busan, Republic of Korea, 2018; Volume 1967, p. 040016. [Google Scholar]
- Luo, X. Research on Convolutional Neural Network Furniture Image Classification Algorithm Based on Feature Fusion. Master’s Thesis, Nanjing University of Posts and Telecommunications, Nanjing, China, 2019. [Google Scholar] [CrossRef]
- Ting-Ting, S.; Ke-Yu, Z.; Hui, Z.; Qiao, H. Interest Points guided Convolution Neural Network for Furniture Styles Classification. In Proceedings of the 2019 6th International Conference on Systems and Informatics (ICSAI), Shanghai, China, 2–4 November 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 1302–1307. [Google Scholar]
- Ataer-Cansizoglu, E.; Liu, H.; Weiss, T.; Mitra, A.; Dholakia, D.; Choi, J.W.; Wulin, D. Room style estimation for style-aware recommendation. In Proceedings of the 2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), San Diego, CA, USA, 9–11 December 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 267–2673. [Google Scholar]
- Zhou, X.; Sun, K.; Wang, J.; Zhao, J.; Feng, C.; Yang, Y.; Zhou, W. Computer Vision Enabled Building Digital Twin Using Building Information Model. IEEE Trans. Ind. Inform. 2022. early access. [Google Scholar] [CrossRef]
- Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A.N.; Polosukhin, I. Attention is all you need. Adv. Neural Inf. Process. Syst. 2017, 30. [Google Scholar] [CrossRef]
- Redmon, J.; Divvala, S.; Girshick, R.; Farhadi, A. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016; pp. 779–788. [Google Scholar]
- Liu, W.; Anguelov, D.; Erhan, D.; Szegedy, C.; Reed, S.; Fu, C.Y.; Berg, A.C. Ssd: Single shot multibox detector. In European Conference on Computer Vision; Springer: Cham, Switzerland, 2016; pp. 21–37. [Google Scholar]
- Dalal, N.; Trigggs, B. Histograms of oriented gradient for human detection. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, San Diego, CA, USA, 20–26 June 2005; pp. 886–893. [Google Scholar]
Major Categories | Subclass (Quantity) |
---|---|
Chair | Office chair (1499), dining chair (1667), Chinese chair (806) |
Bed | Chinese bed (1154), European bed (1004) |
Table | Chinese table (882), European table (889) |
Window | Chinese window (674) |
Door | Primary and secondary doors (1354), interior doors (1138), European doors (340), Chinese doors (346) |
Major Categories | Subclass (Quantity) |
---|---|
Chair | Office chair (216), dining chair (129), Chinese chair (96) |
Bed | Chinese bed (63), European bed (57) |
Table | Chinese table (57), European table (45) |
Window | Chinese window (39) |
Door | Primary and secondary doors (84), interior doors (42), European doors (192), Chinese doors (15) |
Network Model | Highest Accuracy | Average Accuracy |
---|---|---|
VGG Net16 | 0.8665 | 0.8091 |
AM-rVGG | 0.8845 | 0.8311 |
Serial Number | Image Category | Quantity | HOG | VGG Net16 | AM_rVGG | AM_rVGG + HOG |
---|---|---|---|---|---|---|
1 | Office chair | 216 | 0.4582 | 0.1279 | 0.1291 | 0.1281 |
2 | Dining chair | 129 | 0.3371 | 0.1425 | 0.1423 | 0.1525 |
3 | Chinese style chair | 96 | 0.0404 | 0.0692 | 0.0689 | 0.0702 |
4 | European bed | 57 | 0.1278 | 0.0858 | 0.0859 | 0.0918 |
5 | Chinese bed | 63 | 0.0337 | 0.0988 | 0.1072 | 0.1076 |
6 | Chinese window | 39 | 0.0168 | 0.0593 | 0.0598 | 0.0611 |
7 | European table | 45 | 0.0454 | 0.0757 | 0.0783 | 0.0791 |
8 | Chinese table | 57 | 0.0227 | 0.0763 | 0.0786 | 0.0755 |
9 | European style door | 192 | 0.0117 | 0.0298 | 0.0296 | 0.0296 |
10 | Interior door | 42 | 0.0965 | 0.0978 | 0.1017 | 0.1082 |
11 | European style door | 15 | 0.0057 | 0.0329 | 0.0312 | 0.0350 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Yang, Y.; Wang, Y.; Zhou, X.; Su, L.; Hu, Q. BIM Style Restoration Based on Image Retrieval and Object Location Using Convolutional Neural Network. Buildings 2022, 12, 2047. https://doi.org/10.3390/buildings12122047
Yang Y, Wang Y, Zhou X, Su L, Hu Q. BIM Style Restoration Based on Image Retrieval and Object Location Using Convolutional Neural Network. Buildings. 2022; 12(12):2047. https://doi.org/10.3390/buildings12122047
Chicago/Turabian StyleYang, Yalong, Yuanhang Wang, Xiaoping Zhou, Liangliang Su, and Qizhi Hu. 2022. "BIM Style Restoration Based on Image Retrieval and Object Location Using Convolutional Neural Network" Buildings 12, no. 12: 2047. https://doi.org/10.3390/buildings12122047