A Method for Detecting Windows from Mobile Lidar Data.

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 facade detection from mobile lidar data. The proposed method combines bottom-up with top-down strategies to extract facade 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 percent and 88.9 percent in the first two datasets, 100 percent for the rest four datasets in terms of completeness rate, and 100 percent correctness rate for all the tested datasets, which demonstrate the effectiveness of the proposed solution for planar facades with rectilinear windows. The application potential includes generation of building facade models with street-level details and texture synthesis for producing realistic occlusion-free facade texture. [ABSTRACT FROM AUTHOR]