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

Abstract

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.