Abstract
Point clouds are a common representation of 3D scenes, and labelled point clouds are necessary as input to many machine learning systems. Current labelling tools, however, are predominantly 2D. A 3D interface would be more natural fit to the task, and we investigate Virtual Reality as a mechanism for point cloud labelling. In contrast to previous studies we find that the choice of 2D or 3D interface is not the determining factor for labelling speed or accuracy. The nature of the task is important, with some tasks being better suited to the 2D or 3D tools.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
BasicAI Inc.: BasicAI: Multisensory Data Platform for Machine Learning (2022). https://www.basic.ai/
Fernandes, D., et al.: Point-cloud based 3D object detection and classification methods for self-driving applications: a survey and taxonomy. Inf. Fusion 68, 161–191 (2021)
Franzluebbers, A., Li, C., Paterson, A., Johnsen, K.: Virtual reality point cloud annotation. In: 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), pp. 886–887. IEEE (2022)
Hart, S.G., Staveland, L.E.: Development of NASA-TLX (task load index): results of empirical and theoretical research. In: Advances in Psychology, vol. 52, pp. 139–183. Elsevier (1988)
Pointly GmbH: 3D point cloud classification: automatic & manual. Pointly (2022). https://pointly.ai/
Remondino, F.: From point cloud to surface: the modeling and visualization problem. Int. Arch. Photogrammetry Remote Sens. Spat. Inf. Sci. 34 (2003)
Supervisely OÜ: Supervisely 3D sensor fusion labelling (2022). https://supervise.ly/labeling-toolbox/3d-lidar-sensor-fusion/
Varga, R., Costea, A., Florea, H., Giosan, I., Nedevschi, S.: Super-sensor for 360-degree environment perception: point cloud segmentation using image features. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 1–8. IEEE (2017)
Wirth, F., Quehl, J., Ota, J., Stiller, C.: PointAtMe: efficient 3D point cloud labeling in virtual reality. In: 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 1693–1698. IEEE (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Venn, L., Mills, S. (2023). A VR Tool for Labelling 3D Data Sets. In: Yan, W.Q., Nguyen, M., Stommel, M. (eds) Image and Vision Computing. IVCNZ 2022. Lecture Notes in Computer Science, vol 13836. Springer, Cham. https://doi.org/10.1007/978-3-031-25825-1_19
Download citation
DOI: https://doi.org/10.1007/978-3-031-25825-1_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-25824-4
Online ISBN: 978-3-031-25825-1
eBook Packages: Computer ScienceComputer Science (R0)