Testing the Effectiveness of Voxels for Structural Analysis

To assess the condition of cultural heritage assets for conservation, reality-based 3D models can be analyzed using FEA (finite element analysis) software, yielding valuable insights into their structural integrity. Three-dimensional point clouds obtained through photogrammetric and laser scanning techniques can be transformed into volumetric data suitable for FEA by utilizing voxels. When directly using the point cloud data in this process, it is crucial to employ the highest level of accuracy. The fidelity of r point clouds can be compromised by various factors, including uncooperative materials or surfaces, poor lighting conditions, reflections, intricate geometries, and limitations in the precision of the instruments. This data not only skews the inherent structure of the point cloud but also introduces extraneous information. Hence, the geometric accuracy of the resulting model may be diminished, ultimately impacting the reliability of any analyses conducted upon it. The removal of noise from point clouds, a crucial aspect of 3D data processing, known as point cloud denoising, is gaining significant attention due to its ability to reveal the true underlying point cloud structure. This paper focuses on evaluating the geometric precision of the voxelization process, which transforms denoised 3D point clouds into volumetric models suitable for structural analyses.