Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning

• URL: http://arxiv.org/abs/2410.08114v2
• Date: Sun, 27 Jul 2025 11:22:45 GMT
• Title: Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
• Authors: Dingkang Liang, Tianrui Feng, Xin Zhou, Yumeng Zhang, Zhikang Zou, Xiang Bai,
• Abstract summary: We propose a novel intrinsic textbfPointGST (textbfGraph textbfSpectral textbfTuning) method for point cloud.<n>Point GST freezes the pre-trained model and introduces a lightweight, trainable Point Cloud Spectral Adapter (PCSA) for fine-tuning parameters in the spectral domain.<n>Experiments on challenging point cloud datasets across various tasks demonstrate that Point GST not only outperforms its fully fine-tuning counterpart but also significantly reduces trainable parameters.
• Score: 49.91297276176978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, leveraging pre-training techniques to enhance point cloud models has become a prominent research topic. However, existing approaches typically require full fine-tuning of pre-trained models to achieve satisfactory performance on downstream tasks, which is storage-intensive and computationally demanding. To address this issue, we propose a novel Parameter-Efficient Fine-Tuning (PEFT) method for point cloud, called \textbf{PointGST} (\textbf{Point} cloud \textbf{G}raph \textbf{S}pectral \textbf{T}uning). PointGST freezes the pre-trained model and introduces a lightweight, trainable Point Cloud Spectral Adapter (PCSA) for fine-tuning parameters in the spectral domain. The core idea is built on two observations: 1) The inner tokens from frozen models might present confusion in the spatial domain; 2) Task-specific intrinsic information is important for transferring the general knowledge to the downstream task. Specifically, PointGST transfers the point tokens from the spatial domain to the spectral domain, effectively de-correlating confusion among tokens by using orthogonal components for separation. Moreover, the generated spectral basis involves intrinsic information about the downstream point clouds, enabling more targeted tuning. As a result, PointGST facilitates the efficient transfer of general knowledge to downstream tasks while significantly reducing training costs. Extensive experiments on challenging point cloud datasets across various tasks demonstrate that PointGST not only outperforms its fully fine-tuning counterpart but also significantly reduces trainable parameters, making it a promising solution for efficient point cloud learning. The code will be made available at https://github.com/jerryfeng2003/PointGST

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