  Point cloud data labeling is considered a time-consuming and expensive task
in autonomous driving, whereas unsupervised learning can avoid it by learning
point cloud representations from unannotated data. In this paper, we propose
UOV, a novel 3D Unsupervised framework assisted by 2D Open-Vocabulary
segmentation models. It consists of two stages: In the first stage, we
innovatively integrate high-quality textual and image features of 2D
open-vocabulary models and propose the Tri-Modal contrastive Pre-training
(TMP). In the second stage, spatial mapping between point clouds and images is
utilized to generate pseudo-labels, enabling cross-modal knowledge
distillation. Besides, we introduce the Approximate Flat Interaction (AFI) to
address the noise during alignment and label confusion. To validate the
superiority of UOV, extensive experiments are conducted on multiple related
datasets. We achieved a record-breaking 47.73% mIoU on the annotation-free
point cloud segmentation task in nuScenes, surpassing the previous best model
by 10.70% mIoU. Meanwhile, the performance of fine-tuning with 1% data on
nuScenes and SemanticKITTI reached a remarkable 51.75% mIoU and 48.14% mIoU,
outperforming all previous pre-trained models.
