Nested spatial data structures for optimal indexing of LiDAR data

Author:

Ogayar-Anguita Carlos J.ORCID,López-Ruiz AlfonsoORCID,Rueda-Ruiz Antonio J.ORCID,Segura-Sánchez Rafael J.ORCID

Funder

European Regional Development Fund

Ministerio de Ciencia, Innovación y Universidades

Universidad de Jaén

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Computer Science Applications,Engineering (miscellaneous),Atomic and Molecular Physics, and Optics

Reference44 articles.

1. Out-of-core construction of sparse voxel octrees: Out-of-core construction of sparse voxel octrees;Baert;Comput. Graph. Forum,2014

2. OGC Testbed-14: Point Cloud Data Handling Engineering Report;Boehler,2018

3. Boehm, J., 2014. File-centric Organization of large LiDAR Point Clouds in a Big Data context. In: Workshop on Processing Large Geospatial Data. Cardiff, UK.

4. Sideloading ingestion of large point clouds into the Apache spark bid data engine;Boehm;ISPRS - Int. Arch. Photogram. Remote Sens. Spatial Inform. Sci.,2016

5. Lidar sensors;Bräunl,2020

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1. Integrating NoSQL, Hilbert Curve, and R*-Tree to Efficiently Manage Mobile LiDAR Point Cloud Data;ISPRS International Journal of Geo-Information;2024-07-14

2. SimLOD: Simultaneous LOD Generation and Rendering for Point Clouds;Proceedings of the ACM on Computer Graphics and Interactive Techniques;2024-05-11

3. GPU‐Accelerated LOD Generation for Point Clouds;Computer Graphics Forum;2023-08-04

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