  Point cloud based 3D deep model has wide applications in many applications
such as autonomous driving, house robot, and so on. Inspired by the recent
prompt learning in natural language processing, this work proposes a novel
Multi-view Vision-Prompt Fusion Network (MvNet) for few-shot 3D point cloud
classification. MvNet investigates the possibility of leveraging the
off-the-shelf 2D pre-trained models to achieve the few-shot classification,
which can alleviate the over-dependence issue of the existing baseline models
towards the large-scale annotated 3D point cloud data. Specifically, MvNet
first encodes a 3D point cloud into multi-view image features for a number of
different views. Then, a novel multi-view prompt fusion module is developed to
effectively fuse information from different views to bridge the gap between 3D
point cloud data and 2D pre-trained models. A set of 2D image prompts can then
be derived to better describe the suitable prior knowledge for a large-scale
pre-trained image model for few-shot 3D point cloud classification. Extensive
experiments on ModelNet, ScanObjectNN, and ShapeNet datasets demonstrate that
MvNet achieves new state-of-the-art performance for 3D few-shot point cloud
image classification. The source code of this work will be available soon.
