  Large-scale pre-trained models have demonstrated impressive performance in
vision and language tasks within open-world scenarios. Due to the lack of
comparable pre-trained models for 3D shapes, recent methods utilize
language-image pre-training to realize zero-shot 3D shape recognition. However,
due to the modality gap, pretrained language-image models are not confident
enough in the generalization to 3D shape recognition. Consequently, this paper
aims to improve the confidence with view selection and hierarchical prompts.
Leveraging the CLIP model as an example, we employ view selection on the vision
side by identifying views with high prediction confidence from multiple
rendered views of a 3D shape. On the textual side, the strategy of hierarchical
prompts is proposed for the first time. The first layer prompts several
classification candidates with traditional class-level descriptions, while the
second layer refines the prediction based on function-level descriptions or
further distinctions between the candidates. Remarkably, without the need for
additional training, our proposed method achieves impressive zero-shot 3D
classification accuracies of 84.44%, 91.51%, and 66.17% on ModelNet40,
ModelNet10, and ShapeNet Core55, respectively. Furthermore, we will make the
code publicly available to facilitate reproducibility and further research in
this area.
