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

Huihui Shao, Jing Bai, Rusong Wu, Jinzhe Jiang, Hongbo Liang

Abstract: Highlights•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.