  Few-shot anomaly detection methods can effectively address data collecting
difficulty in industrial scenarios. Compared to 2D few-shot anomaly detection
(2D-FSAD), 3D few-shot anomaly detection (3D-FSAD) is still an unexplored but
essential task. In this paper, we propose CLIP3D-AD, an efficient 3D-FSAD
method extended on CLIP. We successfully transfer strong generalization ability
of CLIP into 3D-FSAD. Specifically, we synthesize anomalous images on given
normal images as sample pairs to adapt CLIP for 3D anomaly classification and
segmentation. For classification, we introduce an image adapter and a text
adapter to fine-tune global visual features and text features. Meanwhile, we
propose a coarse-to-fine decoder to fuse and facilitate intermediate
multi-layer visual representations of CLIP. To benefit from geometry
information of point cloud and eliminate modality and data discrepancy when
processed by CLIP, we project and render point cloud to multi-view normal and
anomalous images. Then we design multi-view fusion module to fuse features of
multi-view images extracted by CLIP which are used to facilitate visual
representations for further enhancing vision-language correlation. Extensive
experiments demonstrate that our method has a competitive performance of 3D
few-shot anomaly classification and segmentation on MVTec-3D AD dataset.
