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[IEEE TPAMI] Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
| Date | Stars |
|---|---|
| 2026-07-31 | 391 |
| 2026-08-06 | 391 |
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<div align="center"> <h1>Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning🚀</h1> [Dingkang Liang](https://dk-liang.github.io/)<sup>1</sup>\* ,[Tianrui Feng](https://jerryfeng2003.github.io/)<sup>1</sup>\* ,[Xin Zhou](https://lmd0311.github.io/)<sup>1</sup>\* , Yumeng Zhang<sup>2</sup>, [Zhikang Zou](https://bigteacher-777.github.io/)<sup>2</sup>, and [Xiang Bai](https://scholar.google.com/citations?user=UeltiQ4AAAAJ&hl=en)<sup> 1✉️</sup> <sup>1</sup> Huazhong University of Science and Technology, <sup>2</sup> Baidu Inc. (*) equal contribution, (✉️) corresponding author. [](https://arxiv.org/abs/2410.08114) [](https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE) </div> ## News **[2026-04-06]** 🚀 Check our latest CVPR 2026 paper **[PointTPA](https://github.com/H-EmbodVis/PointTPA)** which introduces an input-aware mechanism for efficient scene-level point cloud analysis. We also provide an **[update](https://github.com/H-EmbodVis/PointGST_on_Scene)** for PointGST. **[2025-07-26]** [PointGST](https://arxiv.org/abs/2410.08114) is accepted by TPAMI. 🎉 **[2024-10-10]** [PointGST](https://arxiv.org/abs/2410.08114) is released. 🔥 ## Abstract Recently, leveraging pre-training techniques to enhance point cloud models has become a hot research topic. However, existing approaches typically require full fine-tuning of pre-trained models to achieve satisfied performance on downstream tasks, accompanying storage-intensive and computationally demanding. To address this issue, we propose a novel Parameter-Efficient Fine-Tuning (PEFT) method for point cloud, called **PointGST** (**Point** cloud **G**raph **S**pectral **T**uning). PointGST freezes the pre-trained model and introduces a lightweight, trainable Point Cloud Spectral Adapter (PCSA) to fine-tune parameters in the spectral domain. <div align="center"> <img src="./figure/intro.png" width = "888" align=center /> </div> 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. More importantly, it improves upon a solid baseline by +2.28\%, 1.16\%, and 2.78\%, resulting in 99.48\%, 97.76\%, and 96.18\% on the ScanObjNN OBJ\_BG, OBJ\_OBLY, and PB\_T50\_RS datasets, respectively. This advancement establishes a new state-of-the-art, using only 0.67\% of the trainable parameters. ## Overview <div align="center"> <img src="./figure/pipeline.png" width = "888" align=center /> </div> ## Getting Started ### Installation We recommend using Anaconda for the installation process: ```bash git clone https://github.com/jerryfeng2003/PointGST.git cd PointGST/ ``` ### Requirements ```bash conda create -y -n pgst python=3.9 conda activate pgst pip install torch==2.0.0 torchvision==0.15.1 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu118 pip install -r requirements.txt # Chamfer Distance & emd cd ./extensions/chamfer_dist python setup.py install --user cd ../emd python setup.py install --user # PointNet++ pip install "git+https://github.com/erikwijmans/Pointnet2_PyTorch.git#egg=pointnet2_ops&subdirectory=pointnet2_ops_lib" # GPU kNN pip install --upgrade https://github.com/unlimblue/KNN_CUDA/releases/download/0.2/KNN_CUDA-0.2-py3-none-any.whl ``` ### Datasets See [DATASET.md](./DATASET.md) for details. ## Main Results <div align="center"> <img src="./figure/result.png" width = "888" align=center /> </div> <div align="center"> <img src="./figure/result2.png" width = "888" align=center /> </div> | Baseline | Trainable Parameters | Dataset | Config | Acc. | Download | | :---- | :---- | :---- | :---- | :---- | :---- | |
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matched fp:cf72e80be42b7a22, desc:fine-tuning, desc:fine tuning, desc:parameter-efficient