# Geometric Characterization of Vines from 3D Point Clouds Obtained with Laser Scanner Systems
## 3 Results
### Volume Calculation Results  
The processing parameters for volume calculations with OctoMap, CHC, and VCTS are reviewed in Table 9.  
These parameters were chosen in an iterative, manual, and supervised process of selection based on the improvement of the results. The visual interpretation of the three-dimensional modelling of each algorithm (whether in the form of voxels, the clustering of points, or simulation of internal structures), and the approximation of the estimated volume to the actual volume of the VSAO, were the criteria for optimizing these values. The OctoMap algorithm needs to know the precise location of the sensor with respect to each scanned point in order to determine if the resulting voxels represent occupied or undefined space, and, therefore, makes it impossible for us to calculate the volume enclosed by the clouds of points taken with MMS (with this algorithm), as in [60]. In addition, the density of the points attained with MMS does not allow us to calculate the volume of the vines with the CHC algorithm, since the dimensions of some parts (i.e., the trunk's diameter) exceed the mean density of the cloud; moreover, its precision is low and it lacks the geometric definition of the figure due to occlusions or a lack of perspective when scanning, as in [60]. The results of these volume calculations are shown in Table 10. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Clipping Parameters** & **TLS \({}^{2}\) Test Area** & **MMS \({}^{3}\) Test Area** & **MMS Real Case** \\ \hline ROI 1 & computed@half & computed@half & computed@half \\  & MeanDistance & MeanDistance & MeanDistance \\ Trunk buffer from strip & 0.3 m & 0.3 m & 0.3 m \\ Trunk buffer in strip & 0.3 m & 0.3 m & 0.3 m \\ Minimum foliage height from terrain & 0.4 m & 0.4 m & 0.4 m \\ Maximum foliage height from terrain & 1.2 m & 1.2 m & 1.2 m \\ Foliage buffer from strip & 0.6 m & 0.6 m & 0.6 m \\ Foliage buffer in strip & ROI buffer & ROI buffer & ROI buffer \\ Sensor type & TLS & MMS & MMS \\ \hline
**Point Cloud Classifier Parameters** & **TLS Test Area** & **MMS Test Area** & **MMS Real Case** \\ \hline Class for trunk & 13 & 13 & 13 \\ Hidden nodes & 5 & 5 & 5 \\ Input nodes & 3 & 3 & 3 \\ Iterations & 60 & 60 & 60 \\ Minimum calibration accuracy & 95 & 95 & 95 \\ Output nodes & 1 & 1 & 1 \\ \hline
**Remove Outliers Parameters** & **TLS Test Area** & **MMS Test Area** & **MMS Real Case** \\ \hline Class to use & 13 & 13 & 13 \\ Statistical sample neighbors for SOR 4 & 50 & 50 & 10 \\ algorithm & & & \\ Statistical std threshold for SOR & 2 & 0.5 & 0.5 \\ algorithm & & & \\ Radius minimum neighbors for ROR 5 & 10 & 2 & 6 \\ algorithm & & & \\ Radius search for ROR algorithm & 0.05 & 0.2 & 0.09 \\ \hline \hline \end{tabular}  
* Region of interest. 2 Terrestrial laser scanner. 3 Mobile mapping system. 4 Statistical outlier removal. 5 Radius outlier removal.  
\end{table} 
Table 8: List of the utilized parameters in preprocessing processes. The results obtained follow the four strategies proposed for the estimation of the height and radius of the identified cylinders, as shown in Table 10.  
Considering these results, the maximum error is reached with the OctoMap algorithm for the cloud scanned with TLS. This is a default error of 28.41%, which may be mainly due to the lack of faces on the scanned object. The convex hull cluster algorithm improves the volume estimation from this cloud (with a 6.40% error). However, better approximations are given from MMS clouds with strategies based on the calculation of the mean radius per segment (3.55%) and the line string (2.46%). Figure 8 shows the VSAO (Figure 8a), and the 3D point cloud taken by its TLS (Figure 8b). One can see the lack of scan points where the irrigation pipe is near the VSAO trunk, as well as under its arms. These occluded parts are also visible in the result of the OctoMap algorithm in Figure 8f, where there are no voxels.