GEOMETRIC PRIMITIVE EXTRACTION FROM SEMANTICALLY ENRICHED POINT CLOUDS
Keywords: 3D point cloud, supervised learning, segmentation, primitive extraction, HBIM, cultural heritage
Abstract. 3D point clouds are robust representations of real-world objects and usually contain information about the shape, size, position and radiometry of the scene. However, unstructured point clouds do not directly exploit the full potential of such information and thus, further analysis is commonly required. Especially when dealing with cultural heritage objects which are, typically, described by complex 3D geometries, semantic segmentation is a fundamental step for the automatic identification of shapes, erosions, etc. This paper focuses on the efficient extraction of semantic classes that would support the generation of geometric primitives such as planes, spheres, cylinders, etc. Our semantic segmentation approach relies on supervised learning using a Random Forest algorithm, while the geometric shapes are identified and extracted with the RANSAC model fitting algorithm. In this way the parametric modelling procedure in a HBIM environment is easily enabled. Our experiments show the efficient label transferability of our 3D semantic segmentation approach across different Doric Greek temples, with qualitatively and quantitatively evaluations.