Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models

Recently, pre-trained point cloud models have found extensive applications in downstream tasks like object classification. However, these tasks often require {full fine-tuning} of models and lead to storage-intensive procedures, thus limiting the real applications of pre-trained models. Inspired by the great success of visual prompt tuning (VPT) in vision, we attempt to explore prompt tuning, which serves as an efficient alternative to full fine-tuning for large-scale models, to point cloud pre-trained models to reduce storage costs. However, it is non-trivial to apply the traditional static VPT to point clouds, owing to the distribution diversity of point cloud data. For instance, the scanned point clouds exhibit various types of missing or noisy points. To address this issue, we propose an Instance-aware Dynamic Prompt Tuning (IDPT) for point cloud pre-trained models, which utilizes a prompt module to perceive the semantic prior features of each instance. This semantic prior facilitates the learning of unique prompts for each instance, thus enabling downstream tasks to robustly adapt to pre-trained point cloud models. Notably, extensive experiments conducted on downstream tasks demonstrate that IDPT outperforms full fine-tuning in most tasks with a mere 7\% of the trainable parameters, thus significantly reducing the storage pressure. Code is available at \url{https://github.com/zyh16143998882/IDPT}.