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Using virtual reality in the structural measurement of plantation Pinus radiata


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Widjojo, EA ORCID: 0000-0001-5305-5687 2021 , 'Using virtual reality in the structural measurement of plantation Pinus radiata', Research Master thesis, University of Tasmania.

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Point cloud is a set of data points that is generally used for big data visualisation. Point cloud can render massive and complex data points in 3D space to represent objects or structures. Advanced user interfaces are widely integrated into modern computing devices enabling interaction between human and large data. Virtual Reality (VR) technologies have demonstrated their potential to provide virtual environment as a medium in exploration of large point cloud data, which is crucial in data analysis. VR technologies have showed positive results when integrated as training/simulation to some domains such as economic, military defence, and education. Integrating point cloud data into immersive VR could potentially support structural estimation of point cloud. This research focuses on the structural estimation of the point cloud data in VR using radiata pine plantation data. This research compares task performance between VR-point cloud assessment and field assessment, focusing on radiata pine plantation data. In addition to the task performance comparison, feedback about experience and impression of assessing radiata pine in VR-point cloud was collected from practitioners and analysed both quantitatively and qualitatively. Results from this research are useful to reveal the strengths and weaknesses of the VR-point cloud for structural estimation tasks in radiata pine trees.

Item Type: Thesis - Research Master
Authors/Creators:Widjojo, EA
Keywords: virtual reality; point cloud data; structural measurement; task performance; Pinus radiata
DOI / ID Number: 10.25959/100.00038295
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Copyright 2021 the author

Additional Information:

Chapters 2.2 and 2.3 contain information published as: E. A. Widjojo, E. A., Chinthammit, W., Engelke, U., 2017. Virtual reality-based human-data interaction, 2017 International Symposium on Big Data Visual Analytics (BDVA), Adelaide, SA, 2017, pp. 1-6. doi: 10.1109/BDVA.2017.8114627 Copyright © 2017, IEEE. In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of the University of Tasmania’s products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to to learn how to obtain a License from RightsLink. The actual published article is in the supplementary file attached to this record but is not available for download here.

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