Skip to main content

Space- and Time-Efficient Storage of LiDAR Point Clouds

  • Conference paper
  • First Online:
String Processing and Information Retrieval (SPIRE 2019)

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].

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.asprs.org/.

  2. 2.

    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.

  3. 3.

    http://pnoa.ign.es/productos_lidar.

  4. 4.

    http://www2.isprs.org/commissions/comm4/wg5/benchmark-on-indoor-modelling.html.

  5. 5.

    https://github.com/LAStools/LAStools.

  6. 6.

    https://github.com/LAStools/LAStools/tree/master/LASlib.

References

  1. de Bernardo, G.: New data structures and algorithms for the efficient management of large spatial datasets. Ph.D. thesis, Universidade da Coruña (2014)

    Google Scholar 

  2. 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)

    Google Scholar 

  3. 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

    Chapter  Google Scholar 

  4. Brisaboa, N.R., Ladra, S., Navarro, G.: DACS: bringing direct access to variable-length codes. Inf. Process. Manage. 49, 392–404 (2013)

    Article  Google Scholar 

  5. Brisaboa, N.R., Ladra, S., Navarro, G.: Compact representation of web graphs with extended functionality. Inf. Syst. 39, 152–174 (2014)

    Article  Google Scholar 

  6. Dong, P., Chen, Q.: LiDAR Remote Sensing and Applications. CRC Press, Boca Raton (2017)

    Book  Google Scholar 

  7. 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

    Google Scholar 

  8. Hyyppä, J., et al.: Forest inventory using laser scanning. In: Topographic Laser Ranging and Scanning, pp. 379–412. CRC Press (2018)

    Google Scholar 

  9. Isenburg, M.: Laszip: lossless compression of lidar data. Photogram. Eng. Remote Sens. 79(2), 209–217 (2013)

    Article  Google Scholar 

  10. Jaboyedoff, M., et al.: Use of lidar in landslide investigations: a review. Nat. Hazards 61(1), 5–28 (2012)

    Article  Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. Klinger, A.: Pattern and Search Statistics. Academic Press (1971). https://doi.org/10.1016/B978-0-12-604550-5.50019-5

  13. Ladra, S., Paramá, J.R., Silva-Coira, F.: Scalable and queryable compressed storage structure for raster data. Inf. Syst. 72, 179–204 (2017)

    Article  Google Scholar 

  14. 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

    Article  Google Scholar 

  15. Navarro, G.: Compact Data Structures: A Practical Approach. Cambridge University Press, Cambridge (2016)

    Book  Google Scholar 

  16. Navarro, G., Nekrich, Y., Russo, L.: Space-efficient data-analysis queries on grids. Theoret. Comput. Sci. 482, 60–72 (2013)

    Article  MathSciNet  Google Scholar 

  17. Palomer, A., Ridao, P., Youakim, D., Ribas, D., Forest, J., Petillot, Y.: 3D laser scanner for underwater manipulation. Sensors 18(4), 1086 (2018)

    Article  Google Scholar 

  18. Ribes, A., Boucheny, C.: Eye-dome lighting: a non-photorealistic shading technique. Technical report (04 2011)

    Google Scholar 

  19. Said, A.: Arithmetic coding. In: Lossless Compression Handbook. Elsevier (2002)

    Google Scholar 

  20. Samet, H.: The quadtree and related hierarchical data structures. ACM Comput. Surv. 16, 187–260 (1984). https://doi.org/10.1145/356924.356930

    Article  MathSciNet  Google Scholar 

  21. The American Society for Photogrammetry and Remote Sensing: ASPRS LIDAR Data Exchange Format Standard. Version 1.0. Format Specification (2003)

    Google Scholar 

  22. The American Society for Photogrammetry and Remote Sensing: LAS Specification 1.4 - R14. Format Specification (2019)

    Google Scholar 

  23. 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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fernando Silva-Coira .

Editor information

Editors and Affiliations

Appendix

Appendix

To better understand the nature of the datasets, we show a visualization of PNOA-large in Fig. 3, and visualizations of the point clouds TUB1 and FireBrigade in Fig. 4.

Fig. 3.
figure 3

Visualization of the dataset labeled as Large.

Fig. 4.
figure 4

Visualization of datasets TUB1 and FireBrigade. We include the point cloud visualization and also an eye-dome lighting (EDL) visualization. EDL is a non-photorealistic, image-based shading technique designed to improve depth perception in scientific visualization images [18].

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-32686-9_36

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32685-2

  • Online ISBN: 978-3-030-32686-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics