Ye, C., Zhu, H., Zhang, B., & Chen, T. (2022). A Closer Look at Few-Shot 3D Point Cloud Classification. International Journal of Computer Vision, 131(3), 772–795. https://doi.org/10.1007/s11263-022-01731-4
Abstract:
In recent years, research on few-shot learning (FSL) has been fast-growing in the 2D image domain due to the less requirement for labeled training data and greater generalization for novel classes. However, its application in 3D point cloud data is relatively under-explored. Not only need to distinguish unseen classes as in the 2D domain, 3D FSL is more challenging in terms of irregular structures, subtle inter-class differences, and high intra-class variances when trained on a low number of data. Moreover, different architectures and learning algorithms make it difficult to study the effectiveness of existing 2D FSL algorithms when migrating to the 3D domain. In this work, for the first time, we perform systematic and extensive investigations of directly applying recent 2D FSL works to 3D point cloud related backbone networks and thus suggest a strong learning baseline for few-shot 3D point cloud classification. Furthermore, we propose a new network, Point-cloud Correlation Interaction (PCIA), with three novel plug-and-play components called Salient-Part Fusion (SPF) module, Self-Channel Interaction Plus (SCI+) module, and Cross-Instance Fusion Plus (CIF+) module to obtain more representative embeddings and improve the feature distinction. These modules can be inserted into most FSL algorithms with minor changes and significantly improve the performance. Experimental results on three benchmark datasets, ModelNet40-FS, ShapeNet70-FS, and ScanObjectNN-FS, demonstrate that our method achieves state-of-the-art performance for the 3D FSL task.
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Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - AME Programmatic Funding Scheme
Grant Reference no. : A18A2b0046
This research / project is supported by the Agency for Science, Technology and Research - A*STAR Robot HTPO Seed Fund
Grant Reference no. : C211518008
This research / project is supported by the Economic Development Board (EDB) - OSTIN STDP Grant
Grant Reference no. : S22-19016-STDP
Description:
This is a post-peer-review, pre-copyedit version of an article published in International Journal of Computer Vision. The final authenticated version is available online at: http://dx.doi.org/10.1007/s11263-022-01731-4