Point cloud analysis has achieved significant development and is well-performed in multiple downstream tasks like point cloud classification and segmentation, etc. Being conscious of the simplicity of the position encoding structure in Transformer-based architectures, we attach importance to the position encoding as a high-dimensional part and the patch encoder to offer multi-scale information. Together with the sequential Transformer, the whole module with position encoding comprehensively constructs a multi-scale feature abstraction module that considers both the local parts from the patch and the global parts from center points as position encoding. With only a few parameters, the position embedding module fits the setting of PEFT (Parameter-Efficient Fine-Tuning) tasks pretty well. Thus we unfreeze these parameters as a fine-tuning part. At the same time, we review the existing prompt and adapter tuning methods, proposing a fresh way of prompts and synthesizing them with adapters as dynamic adjustments. Our Proposed method of PEFT tasks, namely PPT, with only 1.05% of parameters for training, gets state-of-the-art results in several mainstream datasets, such as 95.01% accuracy in the ScanObjectNN OBJ_BG dataset. Codes will be released at https://github.com/zsc000722/PPT.

本研究针对点云分析中的位置编码结构简单性问题提出一种新方法，通过构建多尺度特征抽象模块，实现了对局部和全球信息的综合考虑。研究表明，所提出的PPT方法在多个主流数据集上表现出色，仅使用1.05%的参数进行训练，达到最新的最佳成果，具有显著的应用潜力。

用于高效3D表示学习的位置信息提示调优