  Large pre-trained models have had a significant impact on computer vision by
enabling multi-modal learning, where the CLIP model has achieved impressive
results in image classification, object detection, and semantic segmentation.
However, the model's performance on 3D point cloud processing tasks is limited
due to the domain gap between depth maps from 3D projection and training images
of CLIP. This paper proposes DiffCLIP, a new pre-training framework that
incorporates stable diffusion with ControlNet to minimize the domain gap in the
visual branch. Additionally, a style-prompt generation module is introduced for
few-shot tasks in the textual branch. Extensive experiments on the ModelNet10,
ModelNet40, and ScanObjectNN datasets show that DiffCLIP has strong abilities
for 3D understanding. By using stable diffusion and style-prompt generation,
DiffCLIP achieves an accuracy of 43.2\% for zero-shot classification on OBJ\_BG
of ScanObjectNN, which is state-of-the-art performance, and an accuracy of
80.6\% for zero-shot classification on ModelNet10, which is comparable to
state-of-the-art performance.
