3D indoor mapping and scene understanding have seen tremendous progress in recent years due to the rapid development of sensorsystems, reconstruction techniques and semantic segmentation approaches. However, the quality of the acquired data stronglyinfluences the accuracy of both reconstruction and segmentation. In this paper, we direct our attention to the evaluation of themapping 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 andthe resulting semantic segmentation achieved with a Random Forest classifier applied on these features are affected by the qualityof the acquired data. The achieved results indicate that, while allowing for a fast acquisition of room geometries, the HoloLensprovides data with sufficient accuracy for a wide range of applications.