Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
권호 표지
DOI QR코드

DOI QR Code

Key Point Extraction from LiDAR Data for 3D Modeling

3차원 모델링을 위한 라이다 데이터로부터 특징점 추출 방법

  • Lee, Dae Geon (Department of Geoinformation Engineering, Sejong University) ;
  • Lee, Dong-Cheon (Department of Geoinformation Engineering, Sejong University)
  • Received : 2016.08.23
  • Accepted : 2016.10.18
  • Published : 2016.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

LiDAR(Light Detection and Ranging) data acquired from ALS(Airborne Laser Scanner) has been intensively utilized to reconstruct object models. Especially, researches for 3D modeling from LiDAR data have been performed to establish high quality spatial information such as precise 3D city models and true orthoimages efficiently. To reconstruct object models from irregularly distributed LiDAR point clouds, sensor calibration, noise removal, filtering to separate objects from ground surfaces are required as pre-processing. Classification and segmentation based on geometric homogeneity of the features, grouping and representation of the segmented surfaces, topological analysis of the surface patches for modeling, and accuracy assessment are accompanied by modeling procedure. While many modeling methods are based on the segmentation process, this paper proposed to extract key points directly for building modeling without segmentation. The method was applied to simulated and real data sets with various roof shapes. The results demonstrate feasibility of the proposed method through the accuracy analysis.

항공 레이저 스캐너(ALS)로부터 획득한 라이다(LiDAR) 데이터는 지형지물을 모델링하기 위해서 널리 사용되고 있으며, 특히 정밀 3차원 건축물 및 도시모델, 엄밀정사영상 등 고품질의 공간정보를 효율적으로 구축하기 위하여 라이다 데이터를 이용한 3차원 모델링에 관한 연구가 지속적으로 수행되고 있다. 불규칙적으로 분포된 고밀도의 라이다 데이터로부터 객체를 3차원으로 모델링하기 위해서는 시스템 캘리브레이션, 노이즈 제거 및 지면과 객체를 분리하기 위한 필터링, 객체의 종류 및 특성에 따른 데이터 분류, 기하학적 특성 및 동질성에 기반한 데이터 분할, 분할면의 군집화 및 묘사, 분할면의 재구성과 조합에 의한 모델링, 품질검사 등 일련의 복잡한 과정들이 수반된다. 라이다 데이터를 이용한 많은 모델링 방법들은 데이터 분할 과정을 포함하고 있지만, 본 논문에서는 라이다 데이터를 분할하지 않고 객체를 구성하는 중요하고 대표적인 특징점들을 추출하여 건물 모델링에 활용하는 방법을 제안하고 있다. 복잡하고 다양한 건물 형태를 시뮬레이션한 데이터와 실제 데이터에 적용하여 제안한 방법의 타당성 및 정확도를 검증하였다.

Keywords

References

  1. Alexander, C., Smith-Voysey, S., and Jarvis, C., and Tansey, K. (2009), Integrating building footprints and Lidar elevation data to classify roof structures and visualise buildings, Computers, Environment and Urban Systems, Vol. 33, pp. 285–292. https://doi.org/10.1016/j.compenvurbsys.2009.01.009
  2. Awrangjeb, M. and Lu, G. (2008), Robust image corner detection based on the chord-to-point distance accumulation technique, IEEE Transaction on Multimedia, Vol. 10, No. 6, pp. 1059-1072. https://doi.org/10.1109/TMM.2008.2001384
  3. Awrangjeb, M., Ravanbakhsh, M., and Fraser, C. (2010), Automatic detection of residential buildings using Lidar data and multispectral imagery, ISPRS Journal of Photogrammetry and Remote Sensing Vol. 65, No. 5, pp. 457–467. https://doi.org/10.1016/j.isprsjprs.2010.06.001
  4. Bay, H., Tuytelaars, T., and van Gool, L. (2006), SURF: Speeded up robust features, In: Leonardis, A, Bischof, H., and Pinz, A. (eds.), Computer Vision: Proceedings 9th European Conference on Computer Vision Part I, Springer-Verlag, Berlin, Heidelberg, pp. 404-417.
  5. Chen, L., Teo. T, Shao, Y., Lai, Y., and Rau, J. (2004), Fusion of Lidar data and optical imagery for building modeling, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 35, Part B4, pp. 732–737.
  6. Cho, W., Jwa, Y., Chang, H., and Lee, S. (2004), Pseudo-grid based building extraction using airborne Lidar data, International Archive of Photogrammetry and Remote Sensing, Vol. 35, No. B3, pp. 378-381.
  7. Csathó, B., Schenk, T., Lee, D.C., and Filin, S. (1999), Inclusion of multispectral data into object recognition, International Archives of Photogrammetry and Remote Sensing, Vol. 32, No. 7-4-3W6, pp. 53–61.
  8. Filin S. and Pfeifer, N. (2006), Segmentation of airborne laser scanning data using a slope adaptive neighborhood. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 60, No. 2, pp. 71-80. https://doi.org/10.1016/j.isprsjprs.2005.10.005
  9. Förstner, W. and Gülch, E. (1987), A fast operator for detection and precise location of distinct points, corners and centres of circular features, ISPRS Conference on Fast Processing of Photogrammetric Data, Interlaken, Switzerland, pp. 281-305.
  10. Haala, N. and Kada, M. (2010), An update on automatic 3D building reconstruction, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 65, pp. 570–580. https://doi.org/10.1016/j.isprsjprs.2010.09.006
  11. Habib, A., Zhai, R., and Kim, C. (2010), Generation of complex polyhedral building models by integrating stereo-aerial imagery and Lidar data, Photogrammetric Engineering and Remote Sensing, Vol. 75, No. 5, pp. 609-623.
  12. Han, S., Lee, J., and Yu, K. (2007), An approach for segmentation of airborne laser point clouds utilizing scan-line characteristics, ETRI Journal, Vol. 29, No. 5, pp. 641-648 https://doi.org/10.4218/etrij.07.0106.0316
  13. Harris, C. and Stephens, M. (1988), A combined corner and edge detector, Proceedings of the 4th Alvey Vision Conference, University of Manchester, 31 August-2 September, 1988, pp. 147–151.
  14. He, Y. (2015), Automated 3D Building Modelling from Airborne LIDAR Data, Ph.D. dissertation, The University of Melbourne, Australia, 148p.
  15. Kim, C., Habib, A., and Chang, Y. (2008), Automatic generation of digital building models for complex structures from Lidar data, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 37. Part B4. Beijing, pp. 463-468
  16. Lee, D. and Lee, D.C. (2016), Extraction of model key points and shape analysis for object modeling, Proceedings of Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Suwon, Korea, pp. 158-161. (in Korean with English abstract)
  17. Lee, D.C., Jung, H.S., and Yom, J.H. (2007), 3D Building reconstruction and visualization by clustering airborne Lidar data and roof shape analysis, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 25, No. 6-1, pp. 507-516.
  18. Lee, D.C. and Schenk, T. (1998), An adaptive approach for extracting texture information and segmentation, International Archives of Photogrammetry and Remote Sensing, Vol 32, Part 3/1, Columbus, OH, USA, pp. 250-255.
  19. Lee, I. and Schenk, T. (2001), Perceptual organization of laser altimetry data, International Achieves of Photogrammetry and Remote Sensing, Vol. 34, No. 3-W4, pp. 57-65.
  20. Lee, J., Ga, C., Kim, Y., and Lee, B. (2012) 3D building modeling from airborne Lidar data by building model regularization, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 30, No. 4, pp. 353-362. (in Korean with English abstract) https://doi.org/10.7848/ksgpc.2012.30.4.353
  21. Lee, Y., Oh, J., Shin, S., and Cho, W. (2008), The segmentation and the extraction of precise plane equation of building roof plane using 3D Hough transformation of Lidar data, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 26, No. 5, pp. 505-512. (in Korean with English abstract)
  22. Lowe, D. (2004), Distinctive image features from scale-invariant key points. International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  23. Maas, H. and Vosselman, G. (1999), Two algorithms for extracting building models from raw laser altimetry data. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 54, No. 2/3, pp. 153-163. https://doi.org/10.1016/S0924-2716(99)00004-0
  24. Mohammed, N., Ghazi, A., and Mustafa, H. (2013), Positional accuracy testing of Google Earth, International Journal of Multidisciplinary Sciences and Engineering, Vol. 4, No. 6, pp. 6-9.
  25. Mokhtarian, F. and Suomela, R. (1998), Robust image corner detection through curvature scale space, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 12, pp. 1376-1381. https://doi.org/10.1109/34.735812
  26. Moravec, H. (1977), Towards automatic visual obstacle avoidance, Proceedings of the 5th International Joint Conference on Artificial Intelligence, Cambridge, MA, p. 584.
  27. Park, S., Yoo, E., Lee, D.C., and Lee, Y. (2012), 3D shape descriptor for segmenting point cloud data, Journal of Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 30, No. 6-2, pp. 643-651. https://doi.org/10.7848/ksgpc.2012.30.6-2.643
  28. Rosten, E. and Drummond, T. (2006), Machine learning for high speed corner detection, In: Leonardis, A, Bischof, H., and Pinz, A. (eds.), Computer Vision: Proceedings 9th European Conference on Computer Vision Part I, Lecture Notes in Computer Science (LNCS 3951), Springer-Verlag, Berlin, Heidelberg, pp. 430–443.
  29. Rosten, E., Porter, R., and Drummond, T. (2010), Faster and better: a machine learning approach to corner detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 1, pp. 105-119. https://doi.org/10.1109/TPAMI.2008.275
  30. Rottensteiner, F. (2003), Automatic generation of high-quality building models from Lidar data, 3D Reconstruction and Visualization, IEEE Computer Society, Nov./Dec. Issue 2003, pp. 42-50.
  31. Sampath, A. and Shan, J. (2010), Segmentation and reconstruction of polyhedral building roofs from aerial Lidar point clouds, IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 3, pp. 1554-1567. https://doi.org/10.1109/TGRS.2009.2030180
  32. Schenk, T., Csathó, B., and Lee, D.C. (1999), Quality control issues of airborne laser ranging data and accuracy study in an urban area, Internal Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 3W14, pp. 101-108.
  33. Seo, S. (2003), Model-based Automatic Building Extraction from LIDAR and Aerial Imagery, Ph.D. dissertation, The Ohio State University, USA, 139p.
  34. Sun, S. and Carl Savalggio, C. (2012), Complex building roof detection and strict description from Lidar data and orthorectified aerial imagery, IEEE International Geoscience and Remote Sensing, Munich, Germany, pp. 5466- 5469.
  35. Smith, S.M. and Brady, J.M. (1997), SUSAN – A new approach to low level image processing, International Journal of Computer Vision, Vol. 23, No. 1, pp. 45-78. https://doi.org/10.1023/A:1007963824710
  36. Sohn, G. and Dowman, I. (2007), Data fusion of high-resolution satellite imagery and Lidar data for automatic building extraction, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 62, No. 1, pp. 43–63. https://doi.org/10.1016/j.isprsjprs.2007.01.001
  37. Sohn, G., Jwa, Y., Jung, J., and Kim, H. (2012), An implicit regularization for 3D building rooftop modeling using airborne Lidar data, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS Congress, 25 August – 01 September 2012, Melbourne, Australia, pp. 305-310.
  38. Tarsha-Kurdi, F., Landes, T., and Grussenmeyer, P. (2007), Hough-transform and extended RANSAC algorithms for automatic detection of 3D building roof planes from Lidar data, International Archives of Photogrammetry and Remote Sensing, Vol. 36, Part 3/W52, pp. 407-412.
  39. Verma, V., Kumar, R., and Hue, S. (2006), 3D building detection and modeling from aerial Lidar data, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2213-2220.
  40. Vosselman, G., (2002), Fusion of laser scanning data, maps and aerial photographs for building reconstruction, International Geoscience and Remote Sensing Symposium, 24-28 June, 2002, Toronto, Canada, unpaginated CD-ROM.
  41. Vosselman, G. and Dijkman, S. (2001), 3D building model reconstruction from point clouds and ground plans. International Achieves of Photogrammetry and Remote Sensing, Vol. 34, Part 3/W4, pp. 37-43.
  42. Yoo, E. and Lee, D.C. (2016) True orthoimage generation by mutual recovery of occlusion areas, GIScience and Remote Sensing, Vol. 53, No. 2, pp. 227-246. https://doi.org/10.1080/15481603.2015.1128629

Cited by

  1. 공간정보 구축 분야의 모바일 매핑 시스템 활용을 위한 상용 시스템의 특징 및 현황 조사 vol.18, pp.10, 2017, https://doi.org/10.5762/kais.2017.18.10.36
  2. Utilization of SketchUp for Efficient 3D Modeling of Buildings vol.35, pp.5, 2016, https://doi.org/10.7848/ksgpc.2017.35.5.431
  3. 3D 레이저 스캐너와 토털스테이션 통합에 의한 공간정보 구축의 효율성 분석 vol.18, pp.12, 2016, https://doi.org/10.5762/kais.2017.18.12.638
  4. 적외선 영상, 라이다 데이터 및 특성정보 융합 기반의 합성곱 인공신경망을 이용한 건물탐지 vol.38, pp.6, 2020, https://doi.org/10.7848/ksgpc.2020.38.6.635