CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP
share
Contrastive language-image pre-training (CLIP) achieves promising results in 2D zero-shot and few-shot learning. Despite the impressive performance in 2D tasks, applying CLIP to help the learning in 3D scene understanding has yet to be explored. In this paper, we make the first attempt to investigate how CLIP knowledge benefits 3D scene understanding. To this end, we propose CLIP2Scene, a simple yet effective framework that transfers CLIP knowledge from 2D image-text pre-trained models to a 3D point cloud network. We show that the pre-trained 3D network yields impressive performance on various downstream tasks, i.e., annotation-free and fine-tuning with labelled data for semantic segmentation. Specifically, built upon CLIP, we design a Semantic-driven Cross-modal Contrastive Learning framework that pre-trains a 3D network via semantic and spatial-temporal consistency regularization. For semantic consistency regularization, we first leverage CLIP's text semantics to select the positive and negative point samples and then employ the contrastive loss to train the 3D network. In terms of spatial-temporal consistency regularization, we force the consistency between the temporally coherent point cloud features and their corresponding image features. We conduct experiments on the nuScenes and SemanticKITTI datasets. For the first time, our pre-trained network achieves annotation-free 3D semantic segmentation with 20.8% mIoU. When fine-tuned with 1% or 100% labelled data, our method significantly outperforms other self-supervised methods, with improvements of 8% and 1% mIoU, respectively. Furthermore, we demonstrate its generalization capability for handling cross-domain datasets.
READ FULL TEXT