  Recently, many 2D pretrained foundational models have demonstrated impressive
zero-shot prediction capabilities. In this work, we design a novel pipeline for
zero-shot 3D part segmentation, called ZeroPS. It high-quality transfers
knowledge from 2D pretrained foundational models to 3D point clouds. The main
idea of our approach is to explore the natural relationship between multi-view
correspondences and the prompt mechanism of foundational models and build
bridges on it. Our pipeline consists of two components: 1) a self-extension
component that extends 2D groups from a single viewpoint to spatial
global-level 3D groups; 2) a multi-modal labeling component that introduces a
two-dimensional checking mechanism to vote each 2D predicted bounding box to
the best matching 3D part, and a Class Non-highest Vote Penalty function to
refine the Vote Matrix. Additionally, a merging algorithm is included to merge
part-level 3D groups. Extensive evaluation of three zero-shot segmentation
tasks on PartnetE datasets, achieving state-of-the-art results with significant
improvements (+19.6%, +5.2% and +4.9%, respectively) over existing methods. Our
proposed approach does not need any training, fine-tuning or learnable
parameters. It is hardly affected by domain shift. The code will be released.
