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

Understand

Pre-trained point cloud models have found extensive applications in 3D understanding tasks like object classification and part segmentation.

• However, the prevailing strategy of full fine-tuning in downstream tasks leads to large per-task storage overhead for model parameters, which limits the efficiency when applying large-scale pre-trained models.
• Inspired by the recent success of visual prompt tuning (VPT), this paper attempts to explore prompt tuning on pre-trained point cloud models, to pursue an elegant balance between performance and parameter efficiency.
• We find while instance-agnostic static prompting, e.g.