Point-PEFT: Parameter-Efficient Fine-Tuning for 3D Pre-trained Models

TL;DR

Point-PEFT introduces a parameter-efficient framework for fine-tuning 3D pre-trained point cloud models, achieving superior performance with only 5% of trainable parameters by leveraging domain-specific prompts and geometry-aware adapters.

Contribution

The paper proposes Point-PEFT, a novel method for adapting 3D pre-trained models with minimal parameters using prompt tokens and geometry-aware modules, filling a gap in PEFT for 3D models.

Findings

Outperforms full fine-tuning on downstream tasks

Uses only 5% of trainable parameters

Effective in capturing geometric details

Abstract

The popularity of pre-trained large models has revolutionized downstream tasks across diverse fields, such as language, vision, and multi-modality. To minimize the adaption cost for downstream tasks, many Parameter-Efficient Fine-Tuning (PEFT) techniques are proposed for language and 2D image pre-trained models. However, the specialized PEFT method for 3D pre-trained models is still under-explored. To this end, we introduce Point-PEFT, a novel framework for adapting point cloud pre-trained models with minimal learnable parameters. Specifically, for a pre-trained 3D model, we freeze most of its parameters, and only tune the newly added PEFT modules on downstream tasks, which consist of a Point-prior Prompt and a Geometry-aware Adapter. The Point-prior Prompt adopts a set of learnable prompt tokens, for which we propose to construct a memory bank with domain-specific knowledge, and utilize a parameter-free attention to enhance the prompt tokens. The Geometry-aware Adapter aims to aggregate point cloud features within spatial neighborhoods to capture fine-grained geometric information through local interactions. Extensive experiments indicate that our Point-PEFT can achieve better performance than the full fine-tuning on various downstream tasks, while using only 5% of the trainable parameters, demonstrating the efficiency and effectiveness of our approach. Code is released at https://github.com/Ivan-Tang-3D/Point-PEFT.