Doula, Achref ; Güdelhöfer, Tobias ; Matviienko, Andrii ; Mühlhäuser, Max ; Sanchez Guinea, Alejandro (2023)
PointCloudLab: An Environment for 3D Point Cloud Annotation with Adapted Visual Aids and Levels of Immersion.
40th IEEE International Conference on Robotics and Automation (ICRA2023). London, United Kingdom (29.05.2023 - 02.06.2023)
doi: 10.1109/icra48891.2023.10160225
Conference or Workshop Item, Bibliographie

Abstract

The annotation of 3D point cloud datasets is an expensive and tedious task. To optimize the annotation process, recent works have proposed the use of environments with higher levels of immersion in combination with different types of visual aids. However, two problems remain unresolved. First, the proposed environments limit the user to a unique level of immersion and a fixed hardware setup. Second, their design overlooks the interaction effects between the level of immersion and the visual aids on the quality of the annotation process. To address these issues, we propose PointCloudLab, an environment for 3D point cloud annotation that allows the use of different levels of immersion that work in combination with visual aids. Using PointCloudLab, we conducted a controlled experiment (N=20) to investigate the effects of levels of immersion and visual aids on the annotation process. Our findings reveal that higher levels of immersion combined with object-based visual aids lead to a faster and more accurate annotation. Furthermore, we found significant interaction effects between the levels of immersion and the visual aids on the accuracy of the annotation.

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