# Testing the Effectiveness of Voxels for Structural Analysis
## 5 Conclusions  
The Open3D library test provided the best results in creating voxel models from geometrically well-defined and correctly structured 3D point clouds, while oversimplifying point clouds with complex geometry or small details. On the other hand, the algorithm was able to voxelize models created from complete point clouds, but the details were poor due to the proximity of the meshes to particularly complex shapes. The same problem occurred when using the algorithm to create meshes from voxel grid. The reason lies in the Open3D voxelization function \"voxel_down_sample\", which sometimes does not clearly display planes passing through the figure. The problem seems to be a known issue with the library and leads to poor performance of the algorithm output. This issue may be related to the calculated normals of the input point cloud. Further investigation will be conducted in the future. The set-up of the parameters for the voxelization process can be expressed as follows:  
* Voxel Size Tradeoff: A finer voxel size (voxel_size < 0.2) improves surface accuracy but increases memory consumption. * Marching Cubes Complexity: The algorithm operates on a binary grid rather than the raw point cloud, reducing noise but potentially introducing geometric artifacts. * Denoising Optimization: Adjusting std_ratio allows for controlling the tradeoff between data retention and outlier removal. Future work will focus on analyzing and discussing possible reasons for the mediocre results of voxel modeling. The goal is to test different algorithms and use point clouds of various objects differing in shape, geometric complexity, and size to check if there is an algorithm or a tool that would allow starting directly from real 3D point clouds to perform the structural analysis using volumes in FEA, without adding the inherent approximations of the process. It is hoped that testing different algorithms will provide a better understanding of the improvements needed to write scripts that better adapt to the geometric complexity of the analyzed models. It seems that there is not a script, an algorithm, or software that can provide the accuracy required for the proposed pipeline. Another option would be to pre-segment the point cloud and then apply voxelization to create a model more suitable for FEA (subdivided in its specific characteristics of material properties). This process, on the other hand, can introduce other problems and inaccuracies due to the modeling of single parts instead of the complexity of the object as a unique body. Furthermore, as expected, this procedure works better on single objects such as statues because they can be modeled as a closed volume filling the inside with voxels. Buildings need a more careful and complex survey step to acquire both the outside and the inside; the cleaning of the point cloud is longer and must be performed carefully to have a proper, complete, and accurate 3D reproduction of the structure. Only from this kind of data will it be possible to start the voxelization process. Conceptualization, S.G.B. and E.N. ; methodology, S.G.B. and E.N. ; software, S.G.B. and E.N. ; validation, S.G.B. ; formal analysis, S.G.B. and E.N. ; investigation, S.G.B. ; resources, S.G.B. and E.N. ; data curation, S.G.B. and E.N. ; writing--original draft preparation, S.G.B. ; writing--review and editing, S.G.B. and E.N. ; visualization, S.G.B. and E.N. ; supervision, S.G.B. ; funding acquisition, S.G.B. All authors have read and agreed to the published version of the manuscript. This research was funded by PRIN PNRR RESEARCH PROJECTS OF RELEVANT NATIONAL INTEREST--2022 Call for proposals: \"low coSt tools and mach line learniNg for prEdiction of heRitaGe wood's mechanical decay--SINERGY\", grant number P20224 YMW9, PI. [PERSON], University of Salento, co-PI [PERSON], University of Campania [PERSON]. No new data were created or analyzed in this study. Data sharing is not applicable to this article.