# Filtering Organized 3D Point Clouds for Bin Picking Applications
## 6 Results  
The outcome of the preprocessing procedure (described in Section 5.2) in which the table and the bin datapoints are removed, is shown in Figure 12. Generally, all organized 3D point clouds acquired from a bin filled with shiny parts, such as those shown in Figure 8a-c,f, contain a lot of clearly noticeable outliers, such as those shown in Figure 12a. Nonglossy parts, such as those shown in Figure 8d,e, yield relatively clean 3D point clouds with a small number of outliers, as displayed in Figure 12b. Examples of filtering the preprocessed, organized 3D point clouds \(\widetilde{\mathbf{P}}_{n}\) by SOR and the new method run with the nominal values of parameters \(\left(\rho_{thr}^{*},\,d_{max}^{*},\,\Delta d^{*}\right)\) are shown in Figure 13. For other shiny parts, the new method identifies much more outliers than SOR, similar to the results plotted in Figure 13.  
Figure 12: Examples of two preprocessed point clouds \(\widetilde{\mathbf{P}}_{n}\), with table and bin points removed: (**a**) parts shown in Figure 8a; (**b**) parts shown in Figure 8d. The difference between both methods can be also visualized on a binary 2D map of the same size as z-cut \(\mathbf{Z}_{d}\). In Figure 14a, white pixels indicate the locations of outliers labeled by the new procedure run with the nominal values of parameters \(\left(\rho_{thr}^{*},\ d_{max}^{*},\ \Delta d^{*}\right)\). A rectangle marks a subregion of a full mask, which is enlarged in Figure 14b: it shows locations of outliers output by SOR that fall inside the marked subregion. The example of maps shown in Figure 14 are from the dataset containing parts shown in Figure 8c. The maps created for other shiny parts investigated in this study reveal a similar disparity between the numbers of outliers output by both methods. The quantitative metrics \(\alpha\) and \(\beta_{n}\), defined in (3), that are used to compare the filtering efficiency of both methods are shown in Figure 15. To show the consequences of running the new method with values different than the nominal values of parameter \(d_{max}\) and the ratios \(\omega_{n,Lo}\) and \(\omega_{n,Hi}\) defined in (6) are plotted in Figure 16. The two other parameters were set to their nominal values \(\left(\rho_{thr}^{*},\ \Delta d^{*}\right)\), and the ratio \(\omega_{n,Lo}\) in Figure 16a corresponds to the reduced \(d_{max}=0.5\ d_{max}^{*}\) while \(\omega_{n,Hi}\) in Figure 16b corresponds to the larger \(d_{max}=1.5\ d_{max}^{*}\). Figure 13: Example of filtering by (**a**) the new method run with the nominal parameters \(\left(\rho_{thr}^{*},\ d_{max}^{*},\ \Delta d^{*}\right)\); (**b**) SOR. Outliers are plotted in yellow, the remaining part of 3D point cloud in black. RGB image of parts used in this dataset is shown in Figure 8c. Figure 14: 2D maps showing the locations of the outliers output by (**a**) the new method run with the nominal parameters \((\rho_{thr}^{*},\ d_{max}^{*},\ \Delta t^{*})\); (**b**) SOR. Only a portion of the entire map generated by SOR is shown in the rectangular region defined in (**a**). Corresponding 3D plots of outliers are shown in Figure 13.  
The example 3D plots resulting from running the new method with non-default values of the parameter \(d_{max}\) are displayed in Figure 17 for the nonglossy parts shown in Figure 8e. Yellow points mark identified outliers, black points are the remaining datapoints. A plane drawn in grey is added at \(z=z_{thr}\), where \(z_{thr}=z_{max}-d_{max}\) and \(z_{max}\) is the coordinate of the highest point in \(\widetilde{\mathbf{P}}_{n}\), as in (2). Thus, the location of the plane corresponds to the last z-cut \(\mathbf{Z}_{d}\) processed in a loop by the new procedure, as explained in the flowchart in Figure 7. Results obtained for other parts displayed in Figure 8 reveal a similar pattern, i.e., an increasing number of the identified outliers with the increasing \(d_{max}\).