期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2020
卷号：V-1-2020
页码：165-172
DOI：10.5194/isprs-annals-V-1-2020-165-2020
语种：English
出版社：Copernicus Publications
摘要：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.
