## 4 Conclusions and Future Research  
This paper presents a two-stage framework aimed at resolving the problem of using imbalanced point data for deep network segmentation. The use of hybrid-sampling while balancing the focus of the loss function was shown to boost the effectiveness of object segmentation. Intensive experiments were conducted using five objects under a variety of environmental conditions. The separate use of hybrid-sampling or the balanced focus loss function improved segmentation performance by approximately 2.5%. The two-stage framework improved performance by 6\(\sim\)7%. When applied to validation sets, the mIoU values were as follows: XYZRGB point cloud data (98%) and XYZ point cloud data (93%). In addition, the computational effort of the proposed method was also low. Table 2 shows the average prediction time of the proposed method using the number of test data points from 100 trials. Since the number of points in each image varied between 30,000 and 40,000, the average prediction time based on the first-order linear regression on Table 2 varied between 78.39 and 103.54 ms, which was sufficiently fast in a real situation.  
Even though we demonstrated that the proposed method can reduce the number of false positives associated with object segmentation, there are still a number of issues that may influence the performance.  
* Even when there is relative balance between the number of background instances and object instances, there can still be a significant difference between the number of instances associated with large and small objects. This can make it difficult for the network to detect small objects.
* The overfitting problem exists. When there is a high ratio of oversampling and a large number of epochs, the network tends to remember trained object point clouds, which undermines the generalization of the model and hinders segmentation. Thus, experiments must be conducted on that specific dataset to enable the selection of a suitable ratio for sampling.
* Oversampling magnifies noise. Training datasets inevitably contain noise, with the result being that a small proportion of the data points are labeled erroneously. To avoid magnifying the noise, the oversampling ratio should not be set too high. Setting a lower ratio can help to ensure that the network does not learn an excessive number of wrong instances, which would otherwise compromise detection performance.  
In future research, the validation of the proposed two-stage scheme will be conducted on other datasets and segmentation deep networks, such as PointNet++, DGCNN (Dynamic Graph CNN), and GCN (Graph Attention Convolution), using point cloud data. In addition, the relationship between the success of robotic grasps and the mIoU accuracy of object segmentation will be studied.  
Conceptualization, H.-LL; methodology, H.-I.L. and M.C.N.; software, M.C.N.; validation, M.C.N.; formal analysis, H.-LL; investigation, H.-I.L.; resources, H.-I.L.; data curation, M.C.N.; writing--original draft preparation, H.-LL; writing--review and editing, H.-LL; visualization, H.-I.L.; supervision, H.-I.L.; project administration, H.-I.L.; funding acquisition, H.-I.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Taipei University of Technology grant number NTUT-BIT-105-1.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Size (Points)** & **Prediction Time (ms)** \\ \hline
1024 & 4.82 \\
2048 & 7.34 \\
4096 & 12.8 \\
8192 & 25.7 \\
16384 & 44.4 \\
32768 & 84.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average prediction time.