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Structural health monitoring of Cultural Heritage (CH) is a key topic in research, as well as damage identification and failure assessment. Hence, it is mandatory to have a proper documentation as basis for further analysis.3D models from photogrammetric and laser scanning surveys usually provide 3D point clouds that can be converted in meshes. These models can be used for different purposes, from documentation to visualization to structural analysis. The point clouds usually contain noise data due to different causes: non-cooperative material or surfaces, bad lighting, complex geometry, and low accuracy of the instruments utilized. Noise not only deforms the unstructured geometry of the point clouds, but also adds useless information and reduces the geometric accuracy of the mesh model obtained and, consequently, the results of any analysis performed on it. Point cloud denoising has become one of the hot topics of 3D geometric data processing, removing these noise data to recover the ground-truth point cloud, adding smoothing to the ideal surface. These cleaned point clouds can be converted in mesh with different algorithms, some automatically processed by photogrammetric software and then turned into volumes, suitable for different uses, mainly for structural analysis.The paper wants to analyse the geometric accuracy of few automatic processes available into commercial and open-source software for the conversion of superficial 3D meshes into volumetric models that can be used for structural analyses through FEA process.DOI: https://doi.org/10.20365/disegnarecon.32.2024.15