Volume XLII-2/W9
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W9, 631-638, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W9-631-2019
© 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/W9, 631-638, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W9-631-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  31 Jan 2019

31 Jan 2019

AUTOMATED DETECTION AND LAYOUT REGULARIZATION OF SIMILAR FEATURES IN INDOOR POINT CLOUD

M. Previtali1, L. Barazzetti1, and F. Roncoroni2 M. Previtali et al.
  • 1Politecnico di Milano, Department of Architecture, Built Environment and Construction Engineering, Via Ponzio 31, 20133 Milano, Italy
  • 2Politecnico di Milano, Polo territoriale di Lecco, Via Gaetano Previati, 1/c, 23900 Lecco LC, Italy

Keywords: Layout regularization, Pattern detection, Point cloud analysis, Indoor modeling

Abstract. Automated identification of high-level structures in unorganized point cloud of indoor spaces Indoor space is an important aspect of scene analysis that provides essential information for many applications, such as building digitization, indoor navigation and evacuation route planning. In addition, detection of repetition and regularities in the organization indoor environments, such as rooms, can be used to provide a contextual relationship in the reconstruction phase. However, retrieving high-level information is a challenging task due to the unorganized nature of the raw data, poor-quality of the input data that are in many cases contaminated with noise and outliers. in point benefit from the apparent regularities and strong contextual relationships in façades. The main observation exploited in this paper is the fact that building indoor is generally constituted by a set of basic shapes repeated several times in regular layouts. Building elements can be considered as similar if they share a set of features and elements in an idealized layout exhibiting some regularities. Starting from this main assumption a recursive adaptive partitioning of the indoor point cloud is carried out to automatically derive a flexible and hierarchical 3D representation of the building space. The presented methodology is tested on a synthetic dataset with Gaussian noise. The reconstructed pattern shows a close correspondence with the synthetic one showing the viability of the proposed approach.