<p><strong>Abstract.</strong> In the last years, the application of artificial intelligence (Machine Learning and Deep Learning methods) for the classification of 3D point clouds has become an important task in modern 3D documentation and modelling applications. The identification of proper geometric and radiometric features becomes fundamental to classify 2D/3D data correctly. While many studies have been conducted in the geospatial field, the cultural heritage sector is still partly unexplored. In this paper we analyse the efficacy of the geometric covariance features as a support for the classification of Cultural Heritage point clouds. To analyse the impact of the different features calculated on spherical neighbourhoods at various radius sizes, we present results obtained on four different heritage case studies using different features configurations.</p>

ABSTRACT:3D digitization of heritage artefacts, reverse engineering of industrial components or rapid prototyping-driven design are key topics today. Indeed, millions of archaeological finds all over the world need to be surveyed in 3D either to allow convenient investigations by researchers or because they are inaccessible to visitors and scientists or, unfortunately, because they are seriously endangered by wars and terrorist attacks. On the other hand, in case of industrial and design components there is often the need of deformation analyses or physical replicas starting from reality-based 3D digitisations. The paper is aligned with these needs and presents the realization of the ORION (arduinO Raspberry pI rOtating table for image based 3D recostructioN) prototype system, with its hardware and software components, providing critical insights about its modular design. ORION is an image-based 3D reconstruction system based on automated photogrammetric acquisitions and processing. The system is being developed under a collaborative educational project between FBK Trento, the University of Trento and internship programs with high school in the Trentino province (Italy).

<p><strong>Abstract.</strong> Underwater caves represent the most challenging scenario for exploration, mapping and 3D modelling. In such complex environment, unsuitable to humans, highly specialized skills and expensive equipment are normally required. Technological progress and scientific innovation attempt, nowadays, to develop safer and more automatic approaches for the virtualization of these complex and not easily accessible environments, which constitute a unique natural, biological and cultural heritage. </p><p> This paper presents a pilot study realised for the virtualization of '<i>Grotta Giusti</i>' (Fig. 1), an underground semi-submerged cave system in central Italy. After an introduction on the virtualization process in the cultural heritage domain and a review of techniques and experiences for the virtualization of underground and submerged environments, the paper will focus on the employed virtualization techniques. In particular, the developed approach to simultaneously survey the semi-submersed areas of the cave relying on a stereo camera system and the virtualization of the virtual cave will be discussed.</p>

The paper presents an efficient photogrammetric workflow to improve the 3D reconstruction of scenes surveyed by integrating terrestrial and Unmanned Aerial Vehicle (UAV) images. In the last years, the integration of this kind of images has shown clear advantages for the complete and detailed 3D representation of large and complex scenarios. Nevertheless, their photogrammetric integration often raises several issues in the image orientation and dense 3D reconstruction processes. Noisy and erroneous 3D reconstructions are the typical result of inaccurate orientation results. In this work, we propose an automatic filtering procedure which works at the sparse point cloud level and takes advantage of photogrammetric quality features. The filtering step removes low-quality 3D tie points before refining the image orientation in a new adjustment and generating the final dense point cloud. Our method generalizes to many datasets, as it employs statistical analyses of quality feature distributions to identify suitable filtering thresholds. Reported results show the effectiveness and reliability of the method verified using both internal and external quality checks, as well as visual qualitative comparisons. We made the filtering tool publicly available on GitHub.