Pointing It out! Comparing Manual Segmentation of 3D Point Clouds between Desktop, Tablet, and Virtual Reality

Scanning everyday objects with depth sensors is the state-of-the-art approach to generating point clouds for realistic 3D representations. However, the resulting point cloud data suffers from outliers and contains irrelevant data from neighboring objects. To obtain only the desired 3D representation, additional manual segmentation steps are required. In this paper, we compare three different technology classes as independent variables (desktop vs. tablet vs. virtual reality) in a within-subject user study (N = 18) to understand their effectiveness and efficiency for such segmentation tasks. We found that desktop and tablet still outperform virtual reality regarding task completion times, while we could not find a significant difference between them in the effectiveness of the segmentation. In the post hoc interviews, participants preferred the desktop due to its familiarity and temporal efficiency and virtual reality due to its given three-dimensional representation.

Automated summary

Scanning everyday objects with depth sensors is an advanced method for generating realistic 3D representations, but the resulting point cloud data contains outliers and irrelevant data. Additional manual segmentation steps are needed to obtain the desired 3D representation. This paper compares three different technology classes (desktop vs. tablet vs. virtual reality) in a user study to understand their effectiveness and efficiency for segmentation tasks. The results show that desktop and tablet outperform virtual reality in task completion time, but there is no significant difference in the effectiveness of segmentation. Participants preferred desktop for its familiarity and temporal efficiency, and virtual reality for its three-dimensional representation.