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Title: The effect of 2D vs. 3D visualisation on lidar point cloud analysis tasks
Author: Burwell, Claire Leonora
ISNI:       0000 0004 5918 9303
Awarding Body: University of Leicester
Current Institution: University of Leicester
Date of Award: 2016
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The exploitation of human depth perception is not uncommon in visual analysis of data; medical imagery and geological analysis already rely on stereoscopic 3D visualisation. In contrast, 3D scans of the environment are usually represented on a flat, 2D computer screen, although there is potential to take advantage of both (a) the spatial depth that is offered by the point cloud data, and (b) our ability to see stereoscopically. This study explores whether a stereo 3D analysis environment would add value to visual lidar tasks, compared to the standard 2D display. Forty-six volunteers, all with good stereovision and varying lidar knowledge, viewed lidar data in either 2D or in 3D, on a 4m x 2.4m screen. The first task required 2D and 3D measurement of linear lengths of a planar and a volumetric feature, using an interaction device for point selection. Overall, there was no significant difference in the spread of 2D and 3D measurement distributions for both of the measured features. The second task required interpretation of ten features from individual points. These were highlighted across two areas of interest - a flat, suburban area and a valley slope with a mixture of features. No classification categories were offered to the participant and answers were expressed verbally. Two of the ten features (chimney and cliff-face) were interpreted with a better degree of accuracy using the 3D method and the remaining features had no difference in 2D and 3D accuracy. Using the experiment’s data processing and visualisation approaches, results suggest that stereo 3D perception of lidar data does not add value to manual linear measurement. The interpretation results indicate that immersive stereo 3D visualisation does improve the accuracy of manual point cloud classification for certain features. The findings contribute to wider discussions in lidar processing, geovisualisation, and applied psychology.
Supervisor: Jarvis, Claire ; Tansey, Kevin Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available