# Attentional Keypoint Detection on Point Clouds for 3D Object Part Segmentation
## Effectiveness of the Keypoint-Aware Module:  
We provide visual results with and without using the module on the ShapeNet-Part dataset to validate the advantages of the keypoint-aware module, which are shown in Figure 5. From the comparison between Figure 5a,b, we can observe that, through the keypoint detection module, our method achieved more reasonable results, especially in the intersection areas of different parts. For example, the position circled in red is located at the division between the upper and lower parts of the guitar, where there is semantic discontinuity. Traditional methods do not handle this area well, but introducing the concept of keypoints can provide a better solution. In more detail, the keypoint detection module allows the model to identify and focus on critical points in the point cloud that are indicative of important structural or semantic features. In the case of the guitar example, the keypoints help the model to recognize the boundary between the upper and lower parts of the guitar, which is semantically distinct. By focusing on these keypoints, the model can learn a more robust representation of the shape's structure and semantics, thus leading to improved segmentation results, especially in areas where traditional methods may struggle due to the complexity and ambiguity of the features. Figure 4: Qualitative comparison results between using and not using the feature extension module on PointNet++. (**a**) gives the result without this module on PointNet++, (**b**) shows the visualization results of PointNet++ combined with our feature extension module, and (**c**) shows the corresponding ground truth values. The red dashed circles give detailed improvement visualization results. Best viewed in color. Figure 5: Qualitative comparison results with and without the attention-aware module on PointNet++. (**a**) gives the result without this module on PointNet++, (**b**) shows the visualization results of PointNet++ combined with our attention-aware module, and (**c**) shows the corresponding ground truth values. The purple dashed circles give detailed improvement visualization results. Best viewed in color. **Effectiveness of the Attention-Aware Module:** To demonstrate the effectiveness of our attention-aware module, we conducted experiments on the ShapeNet-Part dataset with and without the module. The qualitative results are shown in Figure 6. From left to right, the visualizations present the results from PointNet++ without the attention-aware module, the results from PointNet++ with this module, and the corresponding ground truth values, respectively. From the visualization, we can see that the part of the object within the red dashed circle is complex: part of it is inside, and part of it is an intersection. PointNet++ was unable to distinguish the segmented curve between them. Based on this module, our model could combine the global and local features to form more reasonable feature representations and provide more reasonable prediction results. **Comparisons with the State-Of-The-Art Methods. ** To evaluate the impact of the number of neighbors on performance, we conducted experiments with different numbers of neighbors, as shown in Table 1 and Figure 7. We found that more neighboring points did not always lead to better performance, nor did fewer neighboring points always lead to better performance. We observed that a value of 40 for the number of neighbors yielded the best performance in our experiments. This supports our hypothesis that a balanced combination of global and local features is necessary to achieve optimal results. In Table 2, we present the performance of the three modules of the AKDNet in an ablation study. In this paper, we used PointNet++ as the baseline. First, we provide the results of PointNet++ on the ShapeNet-Part dataset, where it achieved 85.23% performance in the mean intersection over union (mIoU). The ablation experiments were conducted by incrementally adding the three proposed modules and comparing the results with the baseline.