Parameter-efficient Prompt Learning for 3D Point Cloud Understanding
Hongyu Sun Yongcai Wang Wang Chen Haoran Deng Deying Li
This paper presents a parameter-efficient prompt tuning method, named PPT, to
adapt a large multi-modal model for 3D point cloud understanding. Existing
strategies are quite expensive in computation and storage, and depend on
time-consuming prompt engineering. We address the problems from three aspects.
Firstly, a PromptLearner module is devised to replace hand-crafted prompts with
learnable contexts to automate the prompt tuning process. Then, we lock the
pre-trained backbone instead of adopting the full fine-tuning paradigm to
substantially improve the parameter efficiency. Finally, a lightweight
PointAdapter module is arranged near target tasks to enhance prompt tuning for
3D point cloud understanding. Comprehensive experiments are conducted to
demonstrate the superior parameter and data efficiency of the proposed
method.Meanwhile, we obtain new records on 4 public datasets and multiple 3D
tasks, i.e., point cloud recognition, few-shot learning, and part segmentation.
The implementation is available at https://github.com/auniquesun/PPT.