Morphological Operations on Unorganized Point Clouds Using Octree Graphs

Point clouds resulting from digital scanning are increasingly being used in the heritage field to create knowledge-based models, such as building information models (BIM). Nevertheless, the use of digital image processing techniques in point cloud unorganized data is not well explored yet because of the lack of information about point adjacency and corresponding difficulty in structuring and manipulating data. In this study we propose an approach to deal with image processing-like mathematical morphological operations in unorganized point cloud using the octree graph. Our method consists of three main steps, namely: (i) computing the best distance value between points in the cloud; (ii) constructing an octree from point cloud; and (iii) producing a graph using rectangular layout. We exemplify the use of the proposed methodology performing morphological operations in grayscale and color point clouds of historical buildings. Results prove the effectiveness of the octree representation, which can efficiently generate an adjacency map, demonstrated by the resulting images, and has the potential to be applied at different areas of 3D data analysis.