Ontology-based semantic labeling for RGB-D and point cloud datasets

Abstract: Applications of deep learning have recently seen a surge in the field of construction. Supervised semantic segmentation of 2D or 3D data acquired from buildings requires the use of annotated data for training, validation, and testing. Although various datasets have been published targeting this application, they lack a common convention and definitions based on construction ontologies. This work presents a guideline for ontology-based semantic annotation of RGB-D and point cloud datasets for buildings. Such a contribution facilitates the use of deep learning in construction by bridging the gap between this field and computer science.The annotation guideline is available under this link https://gitlab.rhrk.uni-kl.de/kaufmann/humantech-data-annotation.