Journal of KIBIM (한국BIM학회 논문집)

Korean Institute of Building Information Modeling (한국BIM학회)
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Performance Evaluation of Denoising Algorithms for the 3D Construction Digital Map

건설현장 적용을 위한 디지털맵 노이즈 제거 알고리즘 성능평가

  • 박수열 (한국교통대학교 철도융합시스템공학과) ;
  • 김석 (한국교통대학교 철도인프라시스템공학과)
  • Received : 2020.11.10
  • Accepted : 2020.11.11
  • Published : 2020.12.31
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Abstract

In recent years, the construction industry is getting bigger and more complex, so it is becoming difficult to acquire point cloud data for construction equipments and workers. Point cloud data is measured using a drone and MMS(Mobile Mapping System), and the collected point cloud data is used to create a 3D digital map. In particular, the construction site is located at outdoors and there are many irregular terrains, making it difficult to collect point cloud data. For these reasons, adopting a noise reduction algorithm suitable for the characteristics of the construction industry can affect the improvement of the analysis accuracy of digital maps. This is related to various environments and variables of the construction site. Therefore, this study reviewed and analyzed the existing research and techniques on the noise reduction algorithm. And based on the results of literature review, performance evaluation of major noise reduction algorithms was conducted for digital maps of construction sites. As a result of the performance evaluation in this study, the voxel grid algorithm showed relatively less execution time than the statistical outlier removal algorithm. In addition, analysis results in slope, space, and earth walls of the construction site digital map showed that the voxel grid algorithm was relatively superior to the statistical outlier removal algorithm and that the noise removal performance of voxel grid algorithm was superior and the object preservation ability was also superior. In the future, based on the results reviewed through the performance evaluation of the noise reduction algorithm of this study, we will develop a noise reduction algorithm for 3D point cloud data that reflects the characteristics of the construction site.

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References

  1. Chen, J., Fang, Y., Cho, Y. K., Kim, C. (2016), Principal axes descriptor for automated construction equipment classification from point clouds, Journal of Computing in Civil Engineering, 31(2), pp. 1-12.
  2. Fleishman, S., Cohen-Or, D., Silva, C. (2005). Robust moving least-squares fitting with sharp features, Proceedings of ACM SIGGRAPH.
  3. Han, X. F., Jin, J. S., Wang, M. J., Jiang, W. (2017). Guided 3d point cloud filtering, Multimedia Tools and Applications, pp. 1-15.
  4. Han, X. F., Jin, J. S., Wang, M. J., Jiang. W., Gao. L., Xiao. L. (2017). A review of algorithms for filtering the 3D point cloud, ScienceDirect, 57, pp. 103-112.
  5. Huang, H., Li, D., Zhang, H., Ascher, U., Cohen Or, D. (2009). Consolidation of unorganized point clouds for surface reconstruction, ACM Transactions on Graphics, 28(5), Article 176, pp. 1-7.
  6. Huang, H., Wu, S., Gong, M., Cohen-Or, D., Ascher, U., Zhang, H., (2013). Edge-Aware Point Set Resampling. ACM Transactions on Graphics, 32(1) Article9, pp. 1-12.
  7. Jung, S. H., Lee, K. I., Jung, H. K., Chung, Y. S. (2019). A Study of Broad-band Conformal Beam Forming using Moving Least Squares Method, The transactions of The Korean Institute of Electrical Engineers 68(1), pp. 83-89. https://doi.org/10.5370/KIEE.2019.68.1.83
  8. Lee, J. H., Ok, C. E., Choi, H. S., Kim, Y. S. (2010). A Development Priority and Technology Roadmap for Construction Automation, Journal of the Archotectural Institute of Korea Structure & Construction, 26(10), pp. 131-140.
  9. Ministry of Land, Infrastructure and Transport(MOLIT), Smart Construction Technology Roadmap Report, http://www.molit.go.kr (Oct. 31. 2018).
  10. Moon, K., Pyeon, M., Lee, S., Lee, D. (2014). Setting of the operating conditions of stereo CCTV cameras by weather condition, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 32(6), pp. 591-597. https://doi.org/10.7848/ksgpc.2014.32.6.591
  11. Orts-Escolano, S., Morell, V., Garcia-Rodriguez, J., Cazorla, M. (2013). Point cloud data filtering and downsampling using growing neural gas, The 2013 International Joint Conference on Neural Networks, IJCNN, Dallas, TX, pp. 1-8.
  12. Rusu, R. B., Cousins, S. (2011). 3D is here: Point Cloud Library (PCL), Proceedings of the IEEE International Conference on Robotics and Automation, ICRA, Shanghai, China, pp. 9-13.
  13. Rusu, R. B., Marton, Z. C., Blodow, N., Dolha, M., Beetz, M. (2008). Towards 3D Point Cloud Based Object Maps for Household Environments, Robotics and Autonomous Systems Journal. 56(11), pp. 927-941. https://doi.org/10.1016/j.robot.2008.08.005
  14. Tomasi, C., Manduchi, R. (1998). Bilateral Filtering for Gray and Color Images, Proceedings of the 1998 IEEE International Conference on Computer Vision, pp. 1-8.
  15. Zhou, Q.-Y., Park, J., Koltun, V. (2018). Open3D: a modern library for 3D data processing. Technical report, https://arxiv.org/abs/1801.09847.