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Utilizing extended geocodes for handling massive three-dimensional point cloud data

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Abstract

Point clouds have become a primitive and fundamental material for manifold spatial representations. It can precisely render real-world environments as high-density points which include three-dimensional (3D) coordinates (x, y & z) and other features (color, intensity, and so on). Accordingly, various applications, including robot navigation and self-driving, make use of point clouds not only to detect near objects but to comprehend overall geospatial surroundings. However, it is challenging to exploit the point clouds in terms of spatial query processing in traditional database systems because of its enormous volume and nonstructural formats. In this paper, we propose an efficient method for the manipulation of 3D point cloud based on a Discrete Global Grid System (DGGS). As DGGS represents the Earth as hierarchical sequences of equal area/volume tessellations, it provides an accurate partitioning to integrate and analyze big geospatial data, unlike a base64 geohash representation. This study extends our previous DGGS-based encoding/decoding work to process 3D range queries with more than 64 bits for precise 3D coordinates of point clouds. In particular, we apply PH-tree as a multi-resolution tessellation storage and indexing structure for 3D bounding box queries. The experimental results show that our query processing significantly outperforms the baseline with a linear quadtree. Also, we present the encoding/decoding efficiency of converting large Morton codes from geographic coordinates by using the combination of bit interleaving and lookup tables.

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Notes

  1. Apache Hadoop: Open-source software for reliable, scalable, distributed computing: https://hadoop.apache.org

  2. Apache Spark: Unified Analytics Engine for Big Data: https://spark.apache.org

  3. Apache HBase: Hadoop database, a distributed, scalable, big data store: https://hbase.apache.org

  4. MongoDB: The most popular database for modern apps: https://www.mongodb.com

  5. https://pointcloud.pref.shizuoka.jp/

  6. VisualVM: All-in-One Java Troubleshooting Tool: https://visualvm.github.io

  7. Java harness for building, running, and analysing nano/micro/milli/macro benchmarks: http://openjdk.java.net/projects/code-tools/jmh/

  8. Lightning-fast unified analytics engine: https://spark.apache.org

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Acknowledgements

This work was partially supported by the New Energy and Industrial Technology Development Organization (NEDO). Also, this research was supported by a grant(20NSIP-B135746-04) from National Spatial Information Research Program (NSIP) funded by Ministry of Land, Infrastructure and Transport of Korean government.

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Authors and Affiliations

  1. Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan

    Taehoon Kim, Jun Lee, Kyoung-Sook Kim & Akiyoshi Matono

  2. Department of Computer Science and Engineering, Pusan National University, Pusan, Republic of Korea

    Taehoon Kim & Ki-Joune Li

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  1. Taehoon Kim
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  2. Jun Lee
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  3. Kyoung-Sook Kim
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  4. Akiyoshi Matono
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  5. Ki-Joune Li
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Correspondence to Kyoung-Sook Kim.

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This article belongs to the Topical Collection: Special Issue on Artificial Intelligence and Big Data Computing

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Kim, T., Lee, J., Kim, KS. et al. Utilizing extended geocodes for handling massive three-dimensional point cloud data. World Wide Web 24, 1321–1344 (2021). https://doi.org/10.1007/s11280-020-00783-1

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