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- Title
3D INDOOR MAPPING WITH THE MICROSOFT HOLOLENS: QUALITATIVE AND QUANTITATIVE EVALUATION BY MEANS OF GEOMETRIC FEATURES.
- Authors
Weinmann, M.; Jäger, M. A.; Wursthorn, S.; Jutzi, B.; Hübner, P.
- Abstract
3D indoor mapping and scene understanding have seen tremendous progress in recent years due to the rapid development of sensor systems, reconstruction techniques and semantic segmentation approaches. However, the quality of the acquired data strongly influences the accuracy of both reconstruction and segmentation. In this paper, we direct our attention to the evaluation of the mapping capabilities of the Microsoft HoloLens in comparison to high-quality TLS systems with respect to 3D indoor mapping, feature extraction and semantic segmentation. We demonstrate how a set of rather interpretable low-level geometric features and the resulting semantic segmentation achieved with a Random Forest classifier applied on these features are affected by the quality of the acquired data. The achieved results indicate that, while allowing for a fast acquisition of room geometries, the HoloLens provides data with sufficient accuracy for a wide range of applications.
- Subjects
FEATURE extraction; SYSTEMS development; DATA quality
- Publication
ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 2020, Vol 5, Issue 1, p165
- ISSN
2194-9042
- Publication type
Article
- DOI
10.5194/isprs-annals-V-1-2020-165-2020