Proto-FG3D: Prototype-based Interpretable Fine-Grained 3D Shape Classification


Shuxian Ma (University of Jinan), Zihao Dong (University of Jinan), Runmin Cong (Shandong University), Sam Tak-Wu Kwong (Lingnan University), Xiuli Shao (Nankai University)
The 35th British Machine Vision Conference
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Abstract

Deep learning-based multi-view coarse-grained 3D shape classification has achieved remarkable success over the past decade, leveraging the powerful feature learning capabilities of CNN-based and ViT-based backbones. However, as a challenging research area critical for detailed shape understanding, fine-grained 3D classification remains understudied due to the limited discriminative information captured during multi-view feature aggregation, particularly for subtle inter-class variations, class imbalance, and inherent interpretability limitations of parametric model. To address these problems, we propose the first prototype-based framework named Proto-FG3D for fine-grained 3D shape classification, achieving a paradigm shift from parametric softmax to non-parametric prototype-based classification. Firstly, Proto-FG3D establishes joint multi-view and multi-category representation learning via Prototype Association. Secondly, prototypes are refined via Online Clustering, improving both the robustness of multi-view feature allocation and inter-subclass balance. Finally, prototype-guided supervised learning is established to enhance fine-grained discrimination via prototype-view correlation analysis and enables ad-hoc interpretability through transparent case-based reasoning. Experiments on FG3D and ModelNet40 show Proto-FG3D surpasses state-of-the-art methods in accuracy, transparent predictions, and ad-hoc interpretability with visualizations, challenging conventional fine-grained 3D recognition approaches.

Citation

@inproceedings{Ma_2025_BMVC,
author    = {Shuxian Ma and Zihao Dong and Runmin Cong and Sam Tak-Wu Kwong and Xiuli Shao},
title     = {Proto-FG3D: Prototype-based Interpretable Fine-Grained 3D Shape Classification},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_195/paper.pdf}
}


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