Abstract
Abstract. This paper presents a method that automatically detects windows of different shapes, in 3D LiDAR point clouds obtained from mobile terrestrial data acquisition systems in the urban environment. The proposed method first segments out 3D points belonging to the building façade from the 3D urban point cloud and then projects them onto a 2D plane parallel to the building façade. After point inversion within a watertight boundary, windows are segmented out based on geometrical information. The window features/parameters are then estimated exploiting both symmetrically corresponding windows in the façade as well as temporally corresponding windows in successive passages, based on analysis of variance measurements. This unique fusion of information not only accommodates for lack of symmetry but also helps complete missing features due to occlusions. The estimated windows are then used to refine the 3D point cloud of the building façade. The results, evaluated on real data using different standard evaluation metrics, demonstrate the efficacy as well as the technical prowess of the method.
Highlights
Analysis of building facades for 3D building reconstruction and realistic geometrical modeling of the urban environment has lately received considerable attention
Adapting them to our requirements, the within treatment variability SSW is used for symmetrical correspondences while between treatment variability SSB is used for temporal correspondences
In this paper a method has been presented that automatically detects windows and estimates their parameters in 3D LiDAR point clouds obtained from mobile terrestrial data acquisition systems in the urban environment
Summary
Analysis of building facades for 3D building reconstruction and realistic geometrical modeling of the urban environment has lately received considerable attention. Driven by an increasing demand to improve the quality of such applications, some work has been done lately, focusing on the semantic analysis of building facades including detecting and modeling geometric structures like windows and doors. Such applications rely on data acquired from mobile terrestrial data acquisition systems. In (Ali et al, 2008), a method is presented that converts LiDAR data into distance images, and employs image processing techniques like morphological operations and contour analysis to segment windows An overview of the proposed method is presented in Algorithm 1
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