Automatic point cloud building envelope segmentation (auto-cubes) using machine learning

Abstract

Modern retrofit construction practices use 3D point cloud data of the building envelope to obtain the as-built dimensions. However, manual segmentation by a trained professional is required to identify and measure window openings, door openings, and other architectural features, making the use of 3D point clouds labor-intensive. Automatic point Cloud Building Envelope Segmentation (Auto-CuBES) algorithms can significantly reduce the time spent during point cloud segmentation. The Auto-CuBES algorithm inputs a 3D point cloud generated by commonly available surveying equipment and outputs a dimensioned wire-frame model of the building envelope. By leveraging unsupervised machine learning methods in the Auto-CuBES methods facades, windows, and doors can be identified while keeping the number of calibration parameters low. Additionally, some embodiments of Auto-CuBES can generate a heat map of each facade indicating nonplanar characteristics that are valuable for optimization of connections used in overclad envelope retrofits.