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Automated Construction of IndoorGML Data Using Point Cloud

포인트 클라우드를 이용한 IndoorGML 데이터의 자동적 구축

  • Kim, Sung-Hwan (Dept. of Computer Engineering, Pusan National University) ;
  • Li, Ki-Joune (Dept. of Computer Engineering, Pusan National University)
  • Received : 2020.11.18
  • Accepted : 2020.12.10
  • Published : 2020.12.31

Abstract

As the advancement of technologies on indoor positioning systems and measuring devices such as LiDAR (Light Detection And Ranging) and cameras, the demands on analyzing and searching indoor spaces and visualization services via virtual and augmented reality have rapidly increasing. To this end, it is necessary to model 3D objects from measured data from real-world structures. In addition, it is important to store these structured data in standardized formats to improve the applicability and interoperability. In this paper, we propose a method to construct IndoorGML data, which is an international standard for indoor modeling, from point cloud data acquired from LiDAR sensors. After examining considerations that should be addressed in IndoorGML data, we present a construction method, which consists of free space extraction and connectivity detection processes. With experimental results, we demonstrate that the proposed method can effectively reconstruct the 3D model from point cloud.

실내공간에 대한 측위 기술과 함께 LiDAR (Light Detection And Ranging)나 카메라와 같이 공간을 측정 장비가 발달하면서 실내공간에 대한 분석과 탐색, 가상현실이나 증강현실을 통한 시각화 서비스에 대한 수요가 증가하고 있다. 이를 위해서는 실제 세계로부터 측정된 데이터를 이용하여 3차원 객체로 모델링하는 작업이 필요하다. 또한 이렇게 구조화된 데이터의 가용성과 상호운용성을 높이기 위하여 표준화된 규격으로 저장하는 것도 매우 중요하다. 본 논문에서는 LiDAR 장비를 통해 획득한 포인트 클라우드 데이터를 이용하여 실내공간을 표현하기 위한 국제표준인 IndoorGML 데이터를 자동적으로 구축하는 방법을 제안하고자 한다. IndoorGML 데이터를 구성하는 과정에서 고려해야 할 점들을 살펴본 후, 자유공간추출과 연결성 검출 과정으로 이루어진 데이터 구축 과정을 통하여 실제로 IndoorGML 데이터를 구축한다. 실험을 통하여 제안 기법이 입력 포인트 클라우드로부터 3차원 데이터 모델을 효과적으로 재구성할 수 있음을 검증한다.

Keywords

References

  1. Armeni, I., Sener, O., Zamir, A.R., Jiang, H., Brilakis, I., Fischer, M., and Savarese, S. (2016), 3D semantic parsing of large-scale indoor spaces, IEEE Conference on Computer Vision and Pattern Recognition, 27-30 June, Las Vegas, NV, USA, pp.1534-1543.
  2. Claridades, A.R., Lee, J., and Blanco, A. (2018), Using omnidirectional images for semi-automatically generating IndoorGML data, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 36, No. 5, pp. 319-333. https://doi.org/10.7848/KSGPC.2018.36.5.319
  3. Jang, H., Yu. K., and Yang. J.H. (2020), Indoor reconstruction from floorplan images with a deep learning approach, ISPRS International Journal of Geo-Information, Vol. 9, No. 2, pp. 65:1-15. https://doi.org/10.3390/ijgi9110651
  4. Joo, C.H., Kim, J.S., and Li, K.J. (2012), Method for generating IndoorGML derived from CityGML, Proceedings of Korean Society for Geospatial Information Science, 12 October, Seoul, Korea, pp.38-41. (in Korean)
  5. Kang, H.K. and Li, K.J. (2017), A standard indoor spatial data model-OGC IndoorGML and implementation approaches, ISPRS International Journal of Geo-Information, Vol. 6, No. 4, pp. 116:1-25.
  6. Kazhdan, M., Bolitho, M., and Hoppe, H. (2006), Poisson surface reconstruction, The 4th Eurographics Symposium on Geometry Processing, 26-28 June, Cagliari, Italy, pp.61-70.
  7. Li, K.J. and Kim, D.M. (2018), Converting triangulated 3D indoor mesh data to OGC IndoorGML, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 36, No. 6, pp. 499-505. (in Korean with English abstract) https://doi.org/10.7848/KSGPC.2018.36.6.499
  8. Mirvahabi, S.S. and Abbaspour, R.A. (2015), Automatic extraction of IndoorGML core model from OpenStreetMap, International Archive of the Photogrametry, Remote Sensing and Spatial Information Science, Vol.XL-1/W5, pp. 459-462.
  9. Mura, C., Mattausch, O., and Pajarola, R. (2016), Piecewise-planar reconstruction of multi-room interiors with arbitrary wall arrangements, Computer Graphics Forum, Vol. 35, No.7, pp. 179-188. https://doi.org/10.1111/cgf.13015
  10. Nan, L. and Wonka, P. (2017), PolyFit: polygonal surface reconstruction from point clouds, IEEE International Conference on Computer Vision, 22-29 October, Venice, Italy, pp.2372-2380.
  11. Ochmann, S., Vock, R., Wessel, R., and Klein, R. (2016), Automatic reconstruction of parametric building models from indoor point clouds, Computers & Graphics, Vol. 54, pp. 94-103. https://doi.org/10.1016/j.cag.2015.07.008
  12. Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017), PointNet: deep learning on point sets for 3D classification and segmentation, IEEE Conference on Computer Vision and Pattern Recognition, 21-26 July, Honolulu, HI, USA, pp.77-85.
  13. Sanchez, V. and Zakhor, A. (2012), Planar 3D modeling of building interiors from point cloud data, IEEE International Conference on Image Processing, 30 September-3 October, Orlando, FL, USA, pp.1777-1780.
  14. Schops, T., Sattler, T., and Pollefeys, M. (2019), SurfelMeshing: online surfel-based mesh reconstruction, IEEE transactions on pattern analysis and machine intelligence, Vol. 42, No. 10, pp.2494-2507. https://doi.org/10.1109/tpami.2019.2947048
  15. Srivastava, S., Maheshwari, N., and Rajan, K.S. (2018), Towards generating semantically-rich IndoorGML data from architectural plans, International Archive of the Photogrametry, Remote Sensing and Spatial Information Science, Vol. XLII-4, pp. 591-595.
  16. Teo, T.A and Yu, S.C. (2017), The extraction of indoor building information from BIM to OGC IndoorGML, International Archive of the Photogrametry, Remote Sensing and Spatial Information Science, Vol.XLII-4/W2, pp. 167-170.
  17. Williams, F., Schneider, T., Silva, C., Zorin, D., Bruna, J., and Panozzo, D. (2019), Deep geometric prior for surface reconstruction, IEEE/CVF Conference on Computer Vision and Pattern Recognition, 15-20 June, Long Beach, CA, USA, pp.10122-10131.