Detection of defects in building walls using modified OptD method for down-sampling of point clouds

Terrestrial laser scanning is a simple and nondestructive method for the high-accuracy, three-dimensional mapping of buildings and structures. It yields a high-resolution point cloud, allowing for comprehensive and reliable diagnosis of the target building. However, there are difficulties in processing such large datasets. Commercial software typically reduces the datasets using random methods, resulting in the loss of useful information. Herein, we propose a modified optimum dataset (OptD) method for performing diagnostic measurements on buildings. The modified OptD method allows the retention of more points corresponding to areas of interest, such as those with cracks, cavities, and other surface imperfections, and removal of redundant information related to flat and homogeneous surface walls. We propose two approaches for reducing the size of the datasets while simultaneously detecting the imperfections in building walls. The first is to down-sample the datasets in the OXYZ coordinate system to improve the detection of defects corresponding to geometric changes (e.g. cracks and cavities). The second is to down-sample the datasets in the OXYI coordinate system (where I is the laser intensity) to improve the detection accuracy for defects corresponding to changes in the physicochemical properties of the surface (e.g. moisture content, weathering, salt blooming, and biodeterioration).