Glance and Gaze: Progressive end-to-end deep learning for fine-grained 3D shape classification

Fine-grained 3D shape classification (FGSC) poses a unique challenge due to subtle inter-class differences and intricate structural information, often leading to suboptimal performance. This paper introduces GGView-Net, a novel approach for FGSC utilizing a progressive Glance and Gaze mechanism inspired by human object recognition processes. GGView-Net comprises three key stages - Glance, Gaze, and Joint Classification. The Glance phase mimics the holistic perception of a 3D shape by extracting global features, while the Gaze stage focuses on generating intra-view attention features guided by the global context. Finally, the Joint Classification step tailors losses to individual views and FGSC requirements. Through comprehensive exper-iments on FG3D, GGView-Net demonstrates superior FGSC performance using only class labels, with visualization results confirming its interpretability. Moreover, GGView-Net achieves state-of-the-art results in meta-category 3D shape classification on the ModelNet40 dataset, showcasing remarkable versatility.