In the digital Cultural Heritage domain, the ever-increasing availability of 3D point clouds provides the opportunity to rapidly generate detailed 3D scenes to support the restoration, conservation, main-tenance and safeguarding activities of built heritage. The semantic enrichment of these point clouds could support the automatization of the scan-to-BIM processes. In this framework, the use of Artificial Intelligence techniques for the automatic recognition of architectural elements from point clouds can thus provide valuable support. The described methodology allows increasing the Level of Detail in the semantic segmentation of built heritage point clouds compared to the current state-of-the-art through deep neural networks. The main outcome is therefore the first application of DL framework for CH point clouds, with the subsequent implementation of the selected neural network (the DGCNN) for the semantic segmen-tation task. These results also permit to evaluate the pros and cons of this approach, along with future challenges and trend.
Representation Advances And CHallenges association