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We present an automatic approach to window and façade detection from LiDAR (Light Detection And Ranging) data collected from a moving vehicle along streets in urban environments. 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. The application potential includes generation of building facade models with street-level details and texture synthesis for producing realistic occlusion-free façade texture.