Abstract
LiDAR devices obtain a 3D representation of a space. Due to the large size of the resulting datasets, there already exist storage methods that use compression and present some properties that resemble those of compact data structures. Specifically, LAZ format allows accesses to a given datum or portion of the data without having to decompress the whole dataset and provides indexation of the stored data. However, LAZ format still has some drawbacks that need to be addressed. In this work, we propose a new compact data structure for the representation of a cloud of LiDAR points that supports efficient queries, providing indexing capabilities that are superior to those of the LAZ format.
This research has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie [grant agreement No 690941]; from the Ministerio de Economía y Competitividad (PGE and ERDF) [grant numbers TIN2016-78011-C4-1-R; TIN2016-77158-C4-3-R]; and from Xunta de Galicia (co-founded with ERDF) [grant numbers ED431C 2017/58; ED431G/01].
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Given a bitmap B, \(rank_b(B,i)\) is the number of occurrences of bit b in B[1, i] and \(select_b(B,j)\) is the j-th occurrence of bit b in B.
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References
de Bernardo, G.: New data structures and algorithms for the efficient management of large spatial datasets. Ph.D. thesis, Universidade da Coruña (2014)
de Bernardo, G., Álvarez-García, S., Brisaboa, N.R., Navarro, G., Pedreira, O.: Compact querieable representations of raster data. In: Proceedings of the 20th SPIRE, pp. 96–108 (2013)
Brisaboa, N.R., Luaces, M.R., Navarro, G., Seco, D.: A new point access method based on wavelet trees. In: Heuser, C.A., Pernul, G. (eds.) ER 2009. LNCS, vol. 5833, pp. 297–306. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04947-7_36
Brisaboa, N.R., Ladra, S., Navarro, G.: DACS: bringing direct access to variable-length codes. Inf. Process. Manage. 49, 392–404 (2013)
Brisaboa, N.R., Ladra, S., Navarro, G.: Compact representation of web graphs with extended functionality. Inf. Syst. 39, 152–174 (2014)
Dong, P., Chen, Q.: LiDAR Remote Sensing and Applications. CRC Press, Boca Raton (2017)
Grossi, R., Gupta, A., Vitter, J.S.: High-order entropy-compressed text indexes. In: Proceedings of SODA 2003. Society for Industrial and Applied Mathematics, Philadelphia, PA, Baltimore, Maryland, pp. 841–850 ,12–14 January 2003
Hyyppä, J., et al.: Forest inventory using laser scanning. In: Topographic Laser Ranging and Scanning, pp. 379–412. CRC Press (2018)
Isenburg, M.: Laszip: lossless compression of lidar data. Photogram. Eng. Remote Sens. 79(2), 209–217 (2013)
Jaboyedoff, M., et al.: Use of lidar in landslide investigations: a review. Nat. Hazards 61(1), 5–28 (2012)
Khoshelham, K., Vilariño, L.D., Peter, M., Kang, Z., Acharya, D.: The ISPRS benchmark on indoor modelling. Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. 42, 367–372 (2017)
Klinger, A.: Pattern and Search Statistics. Academic Press (1971). https://doi.org/10.1016/B978-0-12-604550-5.50019-5
Ladra, S., Paramá, J.R., Silva-Coira, F.: Scalable and queryable compressed storage structure for raster data. Inf. Syst. 72, 179–204 (2017)
Meagher, D.: Geometric modeling using octree encoding. Computr Graphics Image Process. 19(2), 129–147 (1982). https://doi.org/10.1016/0146-664X(82)90104-6. http://www.sciencedirect.com/science/article/pii/0146664X82901046
Navarro, G.: Compact Data Structures: A Practical Approach. Cambridge University Press, Cambridge (2016)
Navarro, G., Nekrich, Y., Russo, L.: Space-efficient data-analysis queries on grids. Theoret. Comput. Sci. 482, 60–72 (2013)
Palomer, A., Ridao, P., Youakim, D., Ribas, D., Forest, J., Petillot, Y.: 3D laser scanner for underwater manipulation. Sensors 18(4), 1086 (2018)
Ribes, A., Boucheny, C.: Eye-dome lighting: a non-photorealistic shading technique. Technical report (04 2011)
Said, A.: Arithmetic coding. In: Lossless Compression Handbook. Elsevier (2002)
Samet, H.: The quadtree and related hierarchical data structures. ACM Comput. Surv. 16, 187–260 (1984). https://doi.org/10.1145/356924.356930
The American Society for Photogrammetry and Remote Sensing: ASPRS LIDAR Data Exchange Format Standard. Version 1.0. Format Specification (2003)
The American Society for Photogrammetry and Remote Sensing: LAS Specification 1.4 - R14. Format Specification (2019)
Wang, R., Peethambaran, J., Chen, D.: Lidar point clouds to 3-D urban models: a review. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 11(2), 606–627 (2018)
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Ladra, S., Luaces, M.R., Paramá, J.R., Silva-Coira, F. (2019). Space- and Time-Efficient Storage of LiDAR Point Clouds. In: Brisaboa, N., Puglisi, S. (eds) String Processing and Information Retrieval. SPIRE 2019. Lecture Notes in Computer Science(), vol 11811. Springer, Cham. https://doi.org/10.1007/978-3-030-32686-9_36
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