  Generalizable 3D part segmentation is important but challenging in vision and
robotics. Training deep models via conventional supervised methods requires
large-scale 3D datasets with fine-grained part annotations, which are costly to
collect. This paper explores an alternative way for low-shot part segmentation
of 3D point clouds by leveraging a pretrained image-language model, GLIP, which
achieves superior performance on open-vocabulary 2D detection. We transfer the
rich knowledge from 2D to 3D through GLIP-based part detection on point cloud
rendering and a novel 2D-to-3D label lifting algorithm. We also utilize
multi-view 3D priors and few-shot prompt tuning to boost performance
significantly. Extensive evaluation on PartNet and PartNet-Mobility datasets
shows that our method enables excellent zero-shot 3D part segmentation. Our
few-shot version not only outperforms existing few-shot approaches by a large
margin but also achieves highly competitive results compared to the fully
supervised counterpart. Furthermore, we demonstrate that our method can be
directly applied to iPhone-scanned point clouds without significant domain
gaps.
