Attribute Filtering of Urban Point Clouds Using Max-Tree on Voxel Data

  • Florent GuiotteEmail author
  • Sébastien Lefèvre
  • Thomas Corpetti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11564)


This paper deals with morphological characterization of unstructured 3D point clouds issued from LiDAR data. A large majority of studies first rasterize 3D point clouds onto regular 2D grids and then use standard 2D image processing tools for characterizing data. In this paper, we suggest instead to keep the 3D structure as long as possible in the process. To this end, as raw LiDAR point clouds are unstructured, we first propose some voxelization strategies and then extract some morphological features on voxel data. The results obtained with attribute filtering show the ability of this process to efficiently extract useful information.


Point clouds Max-tree Rasterization Voxel Attribute filtering 


  1. 1.
    Aijazi, A., Checchin, P., Trassoudaine, L.: Segmentation based classification of 3D urban point clouds: a super-voxel based approach with evaluation. Remote Sens. 5(4), 1624–1650 (2013)CrossRefGoogle Scholar
  2. 2.
    Calderon, S., Boubekeur, T.: Point morphology. ACM Trans. Graph. 33(44) (2014)CrossRefGoogle Scholar
  3. 3.
    Dufour, A., et al.: Filtering and segmentation of 3D angiographic data: advances based on mathematical morphology. Med. Image Anal. 17(2), 147–164 (2013)CrossRefGoogle Scholar
  4. 4.
    Ferdosi, B.J., Buddelmeijer, H., Trager, S., Wilkinson, M.H.F., Roerdink, J.B.T.M.: Finding and visualizing relevant subspaces for clustering high-dimensional astronomical data using connected morphological operators. In: IEEE Symposium on Visual Analytics Science and Technology, pp. 35–42 (2010)Google Scholar
  5. 5.
    Géraud, T., Carlinet, E., Crozet, S., Najman, L.: A quasi-linear algorithm to compute the tree of shapes of nD images. In: Hendriks, C.L.L., Borgefors, G., Strand, R. (eds.) ISMM 2013. LNCS, vol. 7883, pp. 98–110. Springer, Heidelberg (2013). Scholar
  6. 6.
    Gorte, B., Pfeifer, N.: Structuring laser-scanned trees using 3D mathematical morphology. Int. Arch. Photogrammetry Remote Sens. 35(B5), 929–933 (2004)Google Scholar
  7. 7.
    Grossiord, E., Talbot, H., Passat, N., Meignan, M., Terve, P., Najman, L.: Hierarchies and shape-space for PET image segmentation. In: IEEE International Symposium on Biomedical Imaging, pp. 1118–1121 (2015)Google Scholar
  8. 8.
    Guiotte, F., Lefevre, S., Corpetti, T.: IEEE/ISPRS Joint Urban Remote Sensing Event (2019)Google Scholar
  9. 9.
    Hernández, J., Marcotegui, B.: Ultimate attribute opening segmentation with shape information. In: Wilkinson, M.H.F., Roerdink, J.B.T.M. (eds.) ISMM 2009. LNCS, vol. 5720, pp. 205–214. Springer, Heidelberg (2009). Scholar
  10. 10.
    Kiwanuka, F.N., Ouzounis, G.K., Wilkinson, M.H.F.: Surface-area-based attribute filtering in 3D. In: Wilkinson, M.H.F., Roerdink, J.B.T.M. (eds.) ISMM 2009. LNCS, vol. 5720, pp. 70–81. Springer, Heidelberg (2009). Scholar
  11. 11.
    Kiwanuka, F.N., Wilkinson, M.H.F.: Radial moment invariants for attribute filtering in 3D. In: Köthe, U., Montanvert, A., Soille, P. (eds.) WADGMM 2010. LNCS, vol. 7346, pp. 68–81. Springer, Heidelberg (2012). Scholar
  12. 12.
    Padilla, F.J.A., et al.: Hierarchical forest attributes for multimodal tumor segmentation on FDG-PET/contrast-enhanced CT. In: IEEE International Symposium on Biomedical Imaging, pp. 163–167 (2018)Google Scholar
  13. 13.
    Peternell, M., Steiner, T.: Minkowski sum boundary surfaces of 3D-objects. Graph. Models 69(3–4), 180–190 (2007)CrossRefGoogle Scholar
  14. 14.
    Roynard, X., Deschaud, J.E., Goulette, F.: Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification. ArXiv e-prints (2017)Google Scholar
  15. 15.
    Salembier, P., Oliveras, A., Garrido, L.: Antiextensive connected operators for image and sequence processing. IEEE Trans. Image Process. 7(4), 555–570 (1998)CrossRefGoogle Scholar
  16. 16.
    Salembier, P., Wilkinson, M.: Connected operators. IEEE Signal Process. Mag. 26(6), 136–157 (2009)CrossRefGoogle Scholar
  17. 17.
    Serna, A., Marcotegui, B., Hernández, J.: Segmentation of facades from urban 3D point clouds using geometrical and morphological attribute-based operators. ISPRS Int. J. Geo-Inf. 5(1), 6 (2016)CrossRefGoogle Scholar
  18. 18.
    Serna, A., Marcotegui, B.: Detection, segmentation and classification of 3D urban objects using mathematical morphology and supervised learning. ISPRS J. Photogrammetry Remote Sens. 93, 243–255 (2014)CrossRefGoogle Scholar
  19. 19.
    Urbach, E., Wilkinson, M.: Shape-only granulometries and grey-scale shape filters. In: International Symposium on Mathematical Morphology, pp. 305–314 (2002)Google Scholar
  20. 20.
    Urien, H., Buvat, I., Rougon, N., Soussan, M., Bloch, I.: Brain lesion detection in 3D PET images using max-trees and a new spatial context criterion. In: Angulo, J., Velasco-Forero, S., Meyer, F. (eds.) ISMM 2017. LNCS, vol. 10225, pp. 455–466. Springer, Cham (2017). Scholar
  21. 21.
    Westenberg, M.A., Roerdink, J.B.T.M., Wilkinson, M.H.F.: Volumetric attribute filtering and interactive visualization using the max-tree representation. IEEE Trans. Image Process. 16(12), 2943–2952 (2007)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Wilkinson, M.H.F., Westenberg, M.A.: Shape preserving filament enhancement filtering. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 770–777. Springer, Heidelberg (2001). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Florent Guiotte
    • 1
    • 2
    Email author
  • Sébastien Lefèvre
    • 2
  • Thomas Corpetti
    • 1
  1. 1.Univ. Rennes 2, LETGRennesFrance
  2. 2.IRISAUniv. Bretagne SudVannesFrance

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