Volume XLII-2/W15
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W15, 133–138, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W15-133-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W15, 133–138, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W15-133-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  20 Aug 2019

20 Aug 2019

ASSESSMENT OF A KEYPOINTS DETECTOR FOR THE REGISTRATION OF INDOOR AND OUTDOOR HERITAGE POINT CLOUDS

R. Assi, T. Landes, A. Murtiyoso, and P. Grussenmeyer R. Assi et al.
  • ICube Laboratory UMR 7357, Photogrammetry and Geomatics Group, National Institute of Applied Sciences (INSA), 24 Boulevard de la Victoire, 67084 Strasbourg CEDEX, France

Keywords: Indoor point clouds, Building information model, Descriptor, Segmentation, Registration

Abstract. In the context of architectural heritage preservation, constructing building information models is an important task. However, conceiving a pertinent model is a difficult, time consuming and user-dependent task. Our laboratory has been researching methods to decrease the time and errors inferred by manual segmentation of point clouds. In the perspective of automatization of the process, we implemented an automated registration method that used only keypoints. Keypoints are special points that hold more information about the global structure of the cloud. In order to detect keypoints, we used the Point Cloud Library (PCL) toolbox. The pertinence of the method was evaluated by registering more than 300 clouds of the zoological museum of Strasbourg. The quality of the keypoint detection was first verified on geo-referenced indoor point clouds. Then we applied this method to register the indoor and outdoor point clouds that have much less area in common; those common points being generally the doors and windows of the façade. The registrations of indoor point clouds were satisfying, with mean distances to the ground truth inferior to 20 cm. While the first result for joint indoor/outdoor registration are promising, it may be improved in future works.