Abstract
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.
This work was financially supported by Région Bretagne (CAMLOT doctoral project).
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References
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)
Calderon, S., Boubekeur, T.: Point morphology. ACM Trans. Graph. 33(44) (2014)
Dufour, A., et al.: Filtering and segmentation of 3D angiographic data: advances based on mathematical morphology. Med. Image Anal. 17(2), 147–164 (2013)
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)
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). https://doi.org/10.1007/978-3-642-38294-9_9
Gorte, B., Pfeifer, N.: Structuring laser-scanned trees using 3D mathematical morphology. Int. Arch. Photogrammetry Remote Sens. 35(B5), 929–933 (2004)
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)
Guiotte, F., Lefevre, S., Corpetti, T.: IEEE/ISPRS Joint Urban Remote Sensing Event (2019)
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). https://doi.org/10.1007/978-3-642-03613-2_19
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). https://doi.org/10.1007/978-3-642-03613-2_7
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). https://doi.org/10.1007/978-3-642-32313-3_5
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)
Peternell, M., Steiner, T.: Minkowski sum boundary surfaces of 3D-objects. Graph. Models 69(3–4), 180–190 (2007)
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)
Salembier, P., Oliveras, A., Garrido, L.: Antiextensive connected operators for image and sequence processing. IEEE Trans. Image Process. 7(4), 555–570 (1998)
Salembier, P., Wilkinson, M.: Connected operators. IEEE Signal Process. Mag. 26(6), 136–157 (2009)
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)
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)
Urbach, E., Wilkinson, M.: Shape-only granulometries and grey-scale shape filters. In: International Symposium on Mathematical Morphology, pp. 305–314 (2002)
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). https://doi.org/10.1007/978-3-319-57240-6_37
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)
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). https://doi.org/10.1007/3-540-45468-3_92
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Guiotte, F., Lefèvre, S., Corpetti, T. (2019). Attribute Filtering of Urban Point Clouds Using Max-Tree on Voxel Data. In: Burgeth, B., Kleefeld, A., Naegel, B., Passat, N., Perret, B. (eds) Mathematical Morphology and Its Applications to Signal and Image Processing. ISMM 2019. Lecture Notes in Computer Science(), vol 11564. Springer, Cham. https://doi.org/10.1007/978-3-030-20867-7_30
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