## 3 Results and Discussion: Qualitative and Quantitative Tool Comparison
### Quantitative Analysis  
To perform the quantitative analysis of the tools discussed throughout the article, we created a 3D model with a point cloud obtained from the images captured by the DJI Mavic 3M (DJI Mavic 3M, more information: [[https://ag.dji.com/es/mavic-3-m](https://ag.dji.com/es/mavic-3-m)]([https://ag.dji.com/es/mavic-3-m](https://ag.dji.com/es/mavic-3-m)) (accessed on 17 February 2024) drone in some fields of El Puig, Valencia. To generate this point cloud, a dataset with 142 images was obtained with a series of flights. Figure 3 shows an orthophoto of the flight. The first parameter that was analyzed was the processing time of photogrammetry to create a 3D point cloud model from the 142 images. These times depend on the characteristics of the machine on which the tools are installed. In our case, all of them were installed on  
Figure 3: Orthophoto generated by ODM of the test flight carried out in May 2023 in El Puig, Valencia. the same machine. The features of that machine were 12 th Gen Intel(R) Core(TM) i7-12700F CPU @2.10 GHz, a 25 MB cache memory, 2 \(\times\) 16 GB DIMM DDR4 3200 RAM, a WD Blue SN570 SSD 1 TB M.2 NVMe drive, Windows 11 \(\times\)64-bit operating system. In terms of the time used by each tool to generate the point cloud, the fastest were DJI Terra, RealityCapture, and ODM; at 14, 20, and 25 min, respectively. Agisoft Metashape, Pix4 dMapper, and 3 df Zephyr took about 40 min. Meshroom and Regard3d took more than an hour. Table 2 shows the number of points in the pointclouds obtained by the different software studied in this paper. To perform the quality study, each model generated by each analyzed tool was saved in the \".ply\" format. These 3D point cloud models were evaluated using the NR-3 DQA [31] tool available on Github (Accessed on 10 November 2023: [[https://github.com/zzc-1998/NR-3](https://github.com/zzc-1998/NR-3) DQA/tree/main]([https://github.com/zzc-1998/NR-3](https://github.com/zzc-1998/NR-3) DQA/tree/main)). Following the guidelines of [31], we extracted the following geometric features from their tool's assortment of metrics, for the objective assessment of point cloud quality:  
* Curvature index (ranges from zero to one): This measurement assesses the degree to which a curve differs from a straight line, commonly indicating roughness or smoothness. Greater values indicate improved quality for the same model. * Anisotropy index (ranges from zero to one): It is the measurement of variations in geometric properties in different directions. Lower values indicate higher quality for the same model. * Linearity index (ranges from zero to one): It measures the level of similarity to a straight line. In this context, higher values indicate higher quality for the identical model. * Flatness index (ranges from zero to one): This parameter measures how closely a surface approximates a flat plane. A low value indicates higher quality for a given model. * Sphericity index (ranges from zero to one): It measures how closely an object's shape resembles that of a perfect sphere. Higher values indicate greater quality for the same model. Figures 4-8 show the mean values and the standard deviation of the selected metrics, i.e., curvature, anisotropy, linearity, flatness, and sphericity, for each 3D model obtained with the tools analyzed in this work. When examining the curvature metric (see Figure 4), our evaluation indicated that Agisoft stood out as the top-performing software with a mean value of 0.11, with DJI Terra (0.059) and ODM (0.051) closely following. The remaining analyzed software tools demonstrated quite similar performances in this regard with values near or smaller than 0.04. A similar behavior occurred when we analyzed the anisotropy in Figure 5. Agisoft with a value of 0.78 and DJI Terra and ODM (with values of 0.89 and 0.9, respectively) had lower values than the other tools analyzed, but their standard deviation was lower. The rest of the tools had similar values, except Meshroom, which had a higher standard deviation.