# Testing the Effectiveness of Voxels for Structural Analysis
## 3 Results  
### Denoising  
Each point cloud was created using Agisoft Metashape 2.2 and scaled with a metal ruler alongside several designated metric targets. The denoising algorithm utilized for the point cloud is one of the most important algorithms in modern research [47]. This approach includes a denoiser that assesses the point cloud's score within a three-dimensional framework [16]. It employs deep learning methodologies to train the model and identify the most suitable fitting region for each point. A gradient ascent technique is used to align groups of outliers with the estimated evaluation function. Essentially, this method simulates an intelligent smoothing of the underlying surface based on majority selection or the density and size of points. The workflow comprises several stages: a score estimation network, an objective function for training it, and a score-based denoising algorithm. The first component is a custom network that evaluates the scoring function around each point. The training objective focuses on comparing the predicted scores from the network with the actual ground truth scores (denoised point cloud points). Ultimately, the score-based denoising algorithm modifies the position of each point through gradient rake. The methodology includes the following: (i) utilizing the Git repository of the original project; (ii) creating an Anaconda environment that fulfills all project requirements and installing the necessary packages; (iii) running a large model denoising algorithm (Large Point Cloud Denoising for over 50,000 Points) on the model, yielding an XYZ mesh output. The system integrates two libraries: pytorch3d for handling 3D components and pytorch for training deep learning models. The latter component functions as the system's core, proficiently extracting features from the input data and relaying them to the evaluation network, which classifies these features utilizing a threshold-based clustering methodology. This process subsequently aligns the data points to meet the threshold criteria and removes outliers, thereby enhancing the model's accuracy. For the comparison of data, after the denoised point clouds have been saved, each original point cloud was juxtaposed with the respective one processed with CloudCompare 2.13 software. By employing a Gaussian distribution defined by its mean and standard deviation, in conjunction with a signed cloud-to-cloud (C2C) distance; only the closest points are identified for comparison. These parameters were chosen due to the denoising algorithm's efficacy in displacing noise points away from the target surface, where most of the points are concentrated, and directing them towards the reference surface. To achieve a more precise assessment of shape variations, profiles were extrapolated along the principal axes to highlight the displacements between the point clouds. The data was exported in *.dxf format and analyzed using Rhinoceros software (Version 8) to determine the maximum distance between the two lines, with the non-denoised point cloud indicated in red and the denoised one in blue. The overlap of these profiles illustrates the algorithm's impact on the geometric arrangement of points, realigning noisy data to conform to the ideal surface. As far as the test objects are concerned (Table 2, Figure 2), the profiles derived from the Samnitte tomb reveal significant discrepancies, with the red line markedly deviating from the actual geometry of the object. This deviation is ascribed to the tomb's surface being covered by ivy and because of the limited survey space. Conversely, the profiles of the statue of [PERSON] exhibit minimal differences, attributed to favorable survey conditions, including compatible material properties, sufficient survey space, and adequate lighting. Table 2 gathers all the main data obtained from the comparisons: mean and standard deviation for each comparison, as shown in Figure 2a,c,e,g,i, and the difference in profiles, visible geometrically in Figure 2b,d,f,h,j.