Technologies and digital tools such as laser scanning and photogrammetry are nowadays widely used in the field of architectural heritage survey, being able of producing 3D models characterized by high metric and morphological accuracy. These databases are becoming essential also for the development of more effective interventions on heritage buildings. Despite the advancement of increasingly automated analytical procedures, the management and analysis of point cloud models can still be quite time-consuming and complex, depending on specific assessments to be carried out. In the direction of optimizing these processing steps, several research is being carried out by applying Artificial Intelligence processes to make predictions based on sample data. The aim of the paper is to analyse point clouds processing focusing on geometric and radiometric features for diagnostic analysis. A specific focus aims at analysing possible in-depth uses of the intensity value as a benchmark for historical surfaces assessment, toward an optimized models’ interpretation and classification of the 3D data points, integrating data and information from different sensors. Point clouds under analysis have been carried out by different acquisition techniques; this provides an interesting opportunity to compare the results in terms of intensity value produced by different sensors. The paper will analyse the State of the Art, also illustrating a set of outcomes obtained by the authors, deepening two specific case studies, in order to outline not only the main background and shortcomings in managing complex database, but also possible innovations pointing out new research questions.
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