Window detection employing a global regularity level set from oblique unmanned aerial vehicle images and point clouds

Detailed building facade structures are significant parts of three-dimensional building models. A photogrammetric point cloud, generated from oblique unmanned aerial system imagery, is a type of data applied in building reconstruction. Its positive characteristics, alongside its challenging qualities, have provoked discussions related to our study. To obtain highly accurate window detection results, we propose a regular window detection method using a global regularity level set. Our method first detects the window boundaries from point clouds using a hole-based boundary extraction method. The minimum bounding rectangle of each boundary point cloud is then used specifically to represent the window. Next, facade partitioning is applied to subdivide the rectified facade images into a series of slices followed by cells. Each cell contains only one window rectangle after the processing. Finally, a global regularity level set algorithm is developed to optimize rectangular windows in horizontal and vertical slices using facade images. The proposed method was validated through a comparison with the slicing method on data from two facades. The accuracy of the window sizes in height and width was also discussed. The experiment results indicate that our method can obtain regular window locations and shapes from photogrammetric point clouds.