  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. Code and datasets are available at
https://github.com/cgye96/A_Closer_Look_At_3DFSL.
