A Method for Detecting Windows from Mobile Lidar Data

Abstract

This article describes how mobile lidar (light detection and ranging) data collection is a rapidly emerging technology where 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. The article presents 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 noise 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. The application potential presented in this article includes the generation of building facade models with street-level details and texture synthesis for producing realistic occlusion-free facade texture.