FGPNet: A weakly supervised fine-grained 3D point clouds classification network

• Published in: Pattern recognition Vol. 139; p. 109509
• Main Authors: Shao, Huihui, Bai, Jing, Wu, Rusong, Jiang, Jinzhe, Liang, Hongbo
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2023
• Subjects: 3D point clouds; Context-aware feature extraction; Fine-grained classification; Local geometric details; SimAM-Capsule aggregation; Spatial relationships; Local geometric details; 3D point clouds; Spatial relationships; SimAM-Capsule aggregation; Context-aware feature extraction; Fine-grained classification
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2023.109509

•In view of the necessity and research value, we are the first to specialize in studying fine-grained classification under the 3D point clouds representation, providing a new perspective for 3D shape classification.•Through in-depth analysis of the characteristics of the target object (3D point clouds) and the key to fine-grained classification tasks, design a feature extraction model to effectively learn the discriminative features.•To highlight discriminative local regions and captures spatial differences between 3D point clouds from different sub-categories, propose a module to capture spatial structure feature and aggerate local features. 3D point clouds classification has been a hot research topic and received great progress in recent years. However, due to the similar data distributions and subtle differences among various sub-categories in a meta-category, the 3D point clouds classification at a fine-grained level is still very challenging, especially without the annotations of part locations or attributes. In this paper, we propose a novel weakly supervised network for fine-grained 3D point clouds classification, namely FGPNet. Different from the previous supervised fine-grained classification methods that use class labels and other manual annotation information, FGPNet develops a unified framework to address both local geometric details and global spatial structures only using the class labels as input. Specifically, FGPNet firstly employs a context-aware discriminative feature extraction (CDFE) module, which extract contextual contrasted information across differential receptive fields hierarchically, and further capture discriminative local details from point clouds. Subsequently, an SimAM-Capsule Aggregation (SCA) module is introduced to highlight the significant local features and capture their spatial relationships. Quantitative and qualitative experimental results on fine-grained dataset including three categories Airplane, Chair and Car demonstrate that FGPNet outperforms the state-of-the-art methods on fine-grained 3D point clouds classification tasks.