This study recognizes the significance and considerable commercial applications in creating Level of Detail (LoD) building models for 3D city models generation. Accordingly, this paper proposes a novel method to identify and extract window frames on building facades from Mobile Laser Scanning (MLS) point clouds. The proposed method can typically be regarded as a stepwise procedure. Firstly, a voxel-based upward-growing method is applied to distinguish non-ground points from ground points. Next, outliers are filtered out from non-ground points by statistical analysis. Then, all the remaining non-ground points are clustered based on the conditional Euclidean clustering algorithm to segment out building facades. A volumetric box is afterward created to store façade points so that neighbors of each point can be operated. Finally, a manipulator is applied according to the structural characteristics of window frames to extract the potential window points. Quantitative evaluations based on 2D validation and 3D validation were both conducted. In the 2D validation, the lowest F1-measure of the test datasets is 0.740, and the highest can be 0.977. While in the 3D validation, the lowest precision of the test dataset is 79.58%, and the highest can be 97.96%. The results demonstrate the proposed method can successfully extract the rectangular or curved windows in the test datasets with promising accuracies to support the generation of LoD3 building models.