Semi-automated minimization of brick-mortar segmentation errors in 3D historical wall reconstruction

In current practices of geometric modeling and building information modeling (BIM) of architectural heritage, point cloud data acquired from site surveys is segmented into parts that correspond to actual building elements. Deep learning methods are used to automate the segmentation processes; however, each historical building presents unique challenges due to distinct construction techniques, irregular applications, deformations, and wear on the surveyed surfaces, and due to these peculiarities, segmented point cloud data may display local errors in the detection of individual architectural elements. This paper presents a method for post-processing segmented point cloud data to semi-automatically detect building elements such as bricks in historical walls. Results show that this approach, which involves testing neighborhoods of points and classified areas to identify and correct misclassified points, diminishes segmentation errors and reconstructs bricks close to their original form.