Towards 10^15-level point clouds management - a nD PointCloud structure

Conference paper (2018)

Authors

H. Liu OLD Department of GIS Technology
P.J.M. van Oosterom OLD Department of GIS Technology
B.M. Meijers OLD Department of GIS Technology
E. Verbree OLD Department of GIS Technology
Research Group
OLD Department of GIS Technology
Copyright: © 2018 H. Liu, P.J.M. van Oosterom, B.M. Meijers, E. Verbree
More Info
expand_more

Published Date

2018

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Research Group

OLD Department of GIS Technology

Abstract

Drastically increasing production of point clouds as well as modern application fields like robotics and virtual reality raises essential demand for smart and highly efficient data management. Effective tools for the managing and direct use of large point clouds are missing. Current state-of-the-art database management systems (DBMS) present critical problems such as inefficient loading/indexing, lack of support of continuous Level of Detail (cLoD) and limited functionalities. Previous research has suggested and demonstrated the importance of converting property dimensions such as time and classification to organizing dimensions for efficient data management at the storage level. However, a thorough validation and theory are still missing. Besides, how new computational platforms such as the cloud technology may support data management also needs further exploration. These problems motivate the PhD research with the focus on a new data structure (nD PointCloud) which is dedicated for smartly and flexibly organizing information of large point clouds for different use cases.