# Attentional Keypoint Detection on Point Clouds for 3D Object Part Segmentation
## 3 Our Approach
### Keypoint-Aware Module  
Currently, numerous methods are attempting to extract key points from point clouds for various tasks. For instance, the Intrinsic Shape Signature (ISS) algorithm, which relies exclusively on the shape's internal geometry, is independent of its position and orientation in space. This algorithm has been utilized by many open-source tools, such as Open3D, for keypoint extraction from point clouds. However, this handcrafted method falls short in accurately detecting key points in complex objects. A method proposed in [53] introduced an unsupervised aligned keypoint detector to obtain key points from the input point cloud. Nevertheless, this approach requires training a separate model for each object category, which can be resource-intensive and time-consuming. To address this issue, LAKe-Net [54] proposes an unsupervised multiscale keypoint detector (UMKD), which is designed to capture key points across all input categories. This approach yields reasonable results for keypoint extraction. However, it treats each stage of the fully connected layer as an independent module, thus disregarding the varying contributions to the final output. The UMKD outputs multiscale coarse point clouds for the input data without considering the relationships between them. In this paper, we introduce a keypoint-aware module to establish relationships among these points, thus enriching the representation of the point cloud. Before delving into the keypoint-aware module, we will first provide a brief overview of LAKe-Net. LAKe-Net is a novel point cloud completion model designed to tackle the challenge of missing topology in partially observed point clouds. The model adopts a novel prediction approach called Keypoints-Skeleton-Shape, which involves three main steps:  
* Aligned Keypoint Localization: An asymmetric keypoint locator is proposed, which consists of an unsupervised multiscale keypoint detector and a complete keypoint generator. The keypoint detector is designed to identify aligned keypoints from both complete and partial point clouds. Theoretical evidence is presented to demonstrate the detector's efficacy in capturing aligned keypoints for objects within a subcategory. * Surface-Skeleton Generation: A new type of skeleton, named Surface-Skeleton, is generated from the localized keypoints based on geometric priors. * Shape Refinement: The model features a refinement subnet, where multiscale surface-skeletons are fed into each recursive skeleton-assisted refinement module. As stated above, the UMKD module effectively addresses the issue of previous methods being needing to train separate keypoint extraction models for multiple types. However, it obtains multiple independent results through multiple softmax operations, which inadvertently increases the subsequent computational load. In this paper, we propose the keypoint-aware module, which learns the weight information of keypoints at different scales to integrate multiple output results, thereby obtaining more effective keypoint information. In more detail, the keypoint-aware module is designed to address the inefficiencies inherent in the UMKD approach by learning a weighting scheme for the keypoints extracted at various scales. Instead of treating each scale independently, the keypoint-aware module considers the interdependencies between different scales, thus allowing for a more coherent and meaningful integration of keypoint information. This not only reduces the computational burden by eliminating the need for multiple softmax operations, but also enhances the quality of the extracted keypoint information, thus leading to improved performance in downstream tasks. For a detailed structure of this module, please refer to Figure 1.  
For simplicity, we make the following assumptions.