Massive point cloud processing on Hadoop: Challenges and proposed solution

Minh Hieu Nguyen, Sanghyun Yoon, Sangyoon Park, Joon Heo

Research output: Contribution to conferencePaperpeer-review

Abstract

Because of the remarkable technological development of sensors and algorithms for point cloud data acquisition and registration, collecting point cloud data over large spaces has become more accessible than ever before. The result of this process is the generation of massive point cloud data, which can exceed the capacity of a single computer. Efficient visualization of this data is an important issue that needs to be addressed. Using big data platforms, such as Hadoop, could bring benefits in processing big point cloud data. However, due to some barriers, there have not been many studies conducted on this platform to solve problems of big point cloud data, so far. In this study, the potential and challenges of processing big point cloud data using Hadoop will be presented. Thereafter, a comprehensive solution will be proposed to overcome the limitations, which can result in the first Hadoop-based framework for fully processing massive point cloud data.

Original languageEnglish
Publication statusPublished - 2020
Event40th Asian Conference on Remote Sensing: Progress of Remote Sensing Technology for Smart Future, ACRS 2019 - Daejeon, Korea, Republic of
Duration: 2019 Oct 142019 Oct 18

Conference

Conference40th Asian Conference on Remote Sensing: Progress of Remote Sensing Technology for Smart Future, ACRS 2019
CountryKorea, Republic of
CityDaejeon
Period19/10/1419/10/18

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) government (Ministry of Science and ICT) (No. 2018R1A2B2009160).

All Science Journal Classification (ASJC) codes

  • Information Systems

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