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International Symposium on String Processing and Information Retrieval

SPIRE 2019: String Processing and Information Retrieval pp 513–527Cite as

Space- and Time-Efficient Storage of LiDAR Point Clouds

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 11811)

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.

Keywords

  • LiDAR point clouds
  • Compression
  • Indexing

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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  • DOI: 10.1007/978-3-030-32686-9_36
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Fig. 1.
Fig. 2.

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.

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Author information

Authors and Affiliations

  1. Universidade da Coruña, CITIC, A Coruña, Spain

    Susana Ladra, Miguel R. Luaces, José R. Paramá & Fernando Silva-Coira

Authors
  1. Susana Ladra
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  2. Miguel R. Luaces
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  3. José R. Paramá
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  4. Fernando Silva-Coira
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Corresponding author

Correspondence to Fernando Silva-Coira .

Editor information

Editors and Affiliations

  1. University of A Coruña, A Coruña, Spain

    Nieves R. Brisaboa

  2. University of Helsinki, Helsinki, Finland

    Dr. Simon J. Puglisi

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.

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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].

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

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