To address the issue of low window-detection completeness and accuracy caused by the irregular distribution of windows on building facades, this study proposes a novel window-detection method that leverages hole constraints and hierarchical localization. This approach utilizes the least-squares method to fit lines to the projected point cloud data of the building facade, with distance constraints applied to obtain the primary wall point cloud data. The initial window position is determined using the hole-based detection method. Incorporating the concept of region expansion, the method employs an improved Alpha-Shape algorithm to extract boundary points around the initially identified window positions. Feature points among the boundary points are identified, and the boundary points are regularized based on these feature points, thus enabling the precise construction of window wireframe models. Experimental results show that this method significantly improves the accuracy of window detection, as evidenced by its average accuracy and completeness of 100% and 93.34%, respectively.