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 this https URL

该研究提出了一种参数高效的提示调优方法（称为 PPT），用于适应大型多模态模型的 3D 点云理解。通过 PromptLearner 模块和 PointAdapter 模块，在提高参数效率的同时增强了对 3D 点云理解的提示调优能力，实验证明了该方法在参数和数据效率方面的优越性。

基于参数高效的 3D 点云理解的提示学习

Parameter-efficient Prompt Learning for 3D Point Cloud Understanding

The recent trend in deep learning methods for 3D point cloud understanding is
to propose increasingly sophisticated architectures either to better capture 3D
geometries or by introducing possibly undesired inductive biases. Moreover,
prior works introducing novel architectures compared their performance on the
same domain, devoting less attention to their generalization to other domains.
We argue that the ability of a model to transfer the learnt knowledge to
different domains is an important feature that should be evaluated to
exhaustively assess the quality of a deep network architecture. In this work we
propose PatchMixer, a simple yet effective architecture that extends the ideas
behind the recent MLP-Mixer paper to 3D point clouds. The novelties of our
approach are the processing of local patches instead of the whole shape to
promote robustness to partial point clouds, and the aggregation of patch-wise
features using an MLP as a simpler alternative to the graph convolutions or the
attention mechanisms that are used in prior works. We evaluated our method on
the shape classification and part segmentation tasks, achieving superior
generalization performance compared to a selection of the most relevant deep
architectures.

本文提出了 PatchMixer 架构，通过处理局部补丁和使用 MLP 聚合补丁特征，将 MLP-Mixer 的思想扩展到 3D 点云中，以提高深度网络架构的泛化性能。