A Method for Detecting Windows from Mobile LiDAR Data

One Sentence: This paper presents a novel method for window detection from mobile LiDAR data. Abstract: Mobile LiDAR (Light Detection And Ranging) data collection is a rapidly emerging technology in which multiple georeferenced sensors (e.g., laser scanners, cameras) are mounted on a moving vehicle to collect real world data. The photorealistic modeling of large-scale real world scenes such as urban environments has become increasingly interesting to the vision, graphics and photogrammetry communities. In this paper, we present an automatic approach to window and façade detection from mobile LiDAR data. The proposed method combines bottom-up with top-down strategies to extract façade planes from noisy LiDAR point clouds. The window detection is achieved through a two-step approach: potential window point detection and window localization. The facade pattern is automatically inferred to enhance the robustness of the window detection. Experimental results on six datasets result in 71.2% and 88.9% in the first two datasets, 100% for the rest four datasets in terms of completeness rate, and 100% correctness rate for all the tested datasets, which demonstrate the effectiveness of the proposed solution for planar façades with rectilinear windows. The application potential includes generation of building facade models with street-level details and texture synthesis for producing realistic occlusion-free façade texture.