Attentional Keypoint Detection on Point Clouds for 3D Object Part Segmentation

Abstract

In the field of computer vision, segmenting a 3D object into its component parts is crucial to understanding its structure and characteristics. Much work has focused on 3D object part segmentation directly from point clouds, and significant progress has been made in this area. This paper proposes a novel 3D object part segmentation method that focuses on integrating three key modules: a keypoint-aware module, a feature extension module, and an attention-aware module. Our approach starts by detecting keypoints, which provide the global feature of the inner shape that serves as the basis for segmentation. Subsequently, we utilize the feature extension module to expand the dimensions, obtain the local representation of the obtained features, provide richer object representation, and improve segmentation accuracy. Furthermore, we introduce an attention-aware module that effectively combines the features of the global and local parts of objects to enhance the segmentation process. To validate the proposed model, we also conduct experiments on the point cloud classification task. The experimental results demonstrate the effectiveness of our method, thus outperforming several state-of-the-art methods in 3D object part segmentation and classification.