Volume XLI-B5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 749–755, 2016
https://doi.org/10.5194/isprs-archives-XLI-B5-749-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 749–755, 2016
https://doi.org/10.5194/isprs-archives-XLI-B5-749-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  16 Jun 2016

16 Jun 2016

EXPLORING REGULARITIES FOR IMPROVING FAÇADE RECONSTRUCTION FROM POINT CLOUDS

K. Zhou1, B. Gorte1, and S. Zlatanova2 K. Zhou et al.
  • 1Dept. of Geoscience and Remote Sensing, TU Delft, the Netherlands
  • 2Dept. of Urbanism, 3D Geoinformation, TU Delft, the Netherlands

Keywords: Terrestrial LiDAR point clouds, Regularities, Windows, Features, Hierarchical clustering, ICP, Chain

Abstract. (Semi)-automatic facade reconstruction from terrestrial LiDAR point clouds is often affected by both quality of point cloud itself and imperfectness of object recognition algorithms. In this paper, we employ regularities, which exist on façades, to mitigate these problems. For example, doors, windows and balconies often have orthogonal and parallel boundaries. Many windows are constructed with the same shape. They may be arranged at the same lines and distance intervals, so do different windows. By identifying regularities among objects with relatively poor quality, these can be applied to calibrate the objects and improve their quality. The paper focuses on the regularities among the windows, which is the majority of objects on the wall. Regularities are classified into three categories: within an individual window, among similar windows and among different windows. Nine cases are specified as a reference for exploration. A hierarchical clustering method is employed to identify and apply regularities in a feature space, where regularities can be identified from clusters. To find the corresponding features in the nine cases of regularities, two phases are distinguished for similar and different windows. In the first phase, ICP (iterative closest points) is used to identify groups of similar windows. The registered points and a number of transformation matrices are used to identify and apply regularities among similar windows. In the second phase, features are extracted from the boundaries of the different windows. When applying regularities by relocating windows, the connections, called chains, established among the similar windows in the first phase are preserved. To test the performance of the algorithms, two datasets from terrestrial LiDAR point clouds are used. Both show good effects on the reconstructed model, while still matching with original point cloud, preventing over or under-regularization.