Acknowledgement
본 연구는 한국철도기술연구원 주요사업 '기존 철도구조물의 BIM기반 유지관리를 위한 역설계 모델링 기술 개발(PK2103C2)'과 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원(No. 2019R1C1C1008326)을 받아 수행되었으며, 이에 깊은 감사를 드립니다.
References
- Hergunsel, M. F. (2011), "Benefits of Building Information Modeling for Construction Managers and BIM based Scheduling", Master's thesis, Worcester Polytechnic Institute.
- Hong, S., Park, I.-S., Heo, J., and Choi, H. (2012), "Indoor 3D Modeling Approach based on Terrestrial LiDAR", KSCE Journal of Civil and Environmental Engineering Research, Vol.32, No.5D, pp.527-532 (in Korean).
- Jaderberg, M., Simonyan, K., Zisserman, A., and Kavukcuoglu, K. (2015), "Spatial transformer networks", Advances in Neural Information Processing Systems.
- Kim, H., Yoon, J., and Sim, S.-H. (2020), "Automated Bridge Component Recognition from Point Clouds Using Deep Learning", Structural Control Health Monitoring, Vol.27, No.9, e2591. https://doi.org/10.1002/stc.2591
- Kreider, R., Messner, J., and Dubler, C. (2010), "Determining the Frequency and Impact of applying BIM for Different Purposes on Projects", Proceedings 6th International Conference on Innovation in Architecture, Engineering and Construction (AEC), pp.1-10.
- Landrieu, L. and Simonovsky, M. (2018), "Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.4558-4567.
- Lee, J.S., Park, J., and Ryu, Y.-M. (2021), "Semantic Segmentation of Bridge Components based on Hierarchical Point Cloud Model", Automation in Construction, Vol.130, 103847. https://doi.org/10.1016/j.autcon.2021.103847
- Lee, O.-G. and Sim, J.-Y. (2021), "Point Cloud Acquisition and Preprocessing based on LiDAR", Broadcasting and Media Magazine, Vol.26, No.2, pp.9-17 (in Korean).
- Meng, J. (2020), "Research on 3D spatial semantic segmentation based on PointNet", Master's thesis, Chonnam National University (in Korean).
- Ministry of Economy and Finance (2020), "The Comprehensive Plan for the Korean New Deal", (in Korean).
- Ministry of Land, Infrastructure and Transport (2018), "Smart Construction Technology Roadmap", (in Korean).
- Park, J. (2021), "Method of Automating Scan-to-BIM Process based on Deep Learning", Master's thesis, Sejong University (in Korean).
- Park, T.-S. and Lee, S.-H. (2015), "Analyses of Existing Tunnel Liner Behaviors Caused by Excavation of Upper Layer with Using Laser Scanning Technology", Journal of the Korean Geotechnical Society, Vol.31, No.10, pp.29-36 (in Korean). https://doi.org/10.7843/KGS.2015.31.10.29
- Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017), "PointNet, Deep Learning on Point Sets from 3D Classification and Segmentation", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.77-85.
- Rao, Y., Zhang, M., Cheng, Z., Xue, J., Pu, J., and Wang, Z. (2021), "Semantic Point Cloud Segmentation Using Fast Deep Neural Network and DCRF", Sensors, Vol.21, 2731. https://doi.org/10.3390/s21082731
- Robert, L. (1986), "Stochastic Sampling in Computer Graphics", ACM Transaction on Graphics, Vol.5, No.1, pp.51-72. https://doi.org/10.1145/7529.8927
- Soilan, M., Novoa, A., Sanchez-Rodriguez, A., Riveiro, B., and Arias, P. (2020), "Semantic Segmentation of Point Clouds with Pointnet and Kpconv Architectures Applied to Railway Tunnels", ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.V-2-2020. pp.281-288. https://doi.org/10.5194/isprs-annals-V-2-2020-281-2020
- Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., and Solomon, J.M. (2019), "Dynamic Graph CNN for Learning on Point Clouds", ACM Transaction on Graphics, Vol.38, No.5, 146.