PointLoRA: Low-Rank Adaptation with Token Selection for Point Cloud Learning
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913860770856960 |
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| author | Wang, Song Liu, Xiaolu Kong, Lingdong Xu, Jianyun Hu, Chunyong Fang, Gongfan Li, Wentong Zhu, Jianke Wang, Xinchao |
| author_facet | Wang, Song Liu, Xiaolu Kong, Lingdong Xu, Jianyun Hu, Chunyong Fang, Gongfan Li, Wentong Zhu, Jianke Wang, Xinchao |
| contents | Self-supervised representation learning for point cloud has demonstrated effectiveness in improving pre-trained model performance across diverse tasks. However, as pre-trained models grow in complexity, fully fine-tuning them for downstream applications demands substantial computational and storage resources. Parameter-efficient fine-tuning (PEFT) methods offer a promising solution to mitigate these resource requirements, yet most current approaches rely on complex adapter and prompt mechanisms that increase tunable parameters. In this paper, we propose PointLoRA, a simple yet effective method that combines low-rank adaptation (LoRA) with multi-scale token selection to efficiently fine-tune point cloud models. Our approach embeds LoRA layers within the most parameter-intensive components of point cloud transformers, reducing the need for tunable parameters while enhancing global feature capture. Additionally, multi-scale token selection extracts critical local information to serve as prompts for downstream fine-tuning, effectively complementing the global context captured by LoRA. The experimental results across various pre-trained models and three challenging public datasets demonstrate that our approach achieves competitive performance with only 3.43% of the trainable parameters, making it highly effective for resource-constrained applications. Source code is available at: https://github.com/songw-zju/PointLoRA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16023 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PointLoRA: Low-Rank Adaptation with Token Selection for Point Cloud Learning Wang, Song Liu, Xiaolu Kong, Lingdong Xu, Jianyun Hu, Chunyong Fang, Gongfan Li, Wentong Zhu, Jianke Wang, Xinchao Computer Vision and Pattern Recognition Self-supervised representation learning for point cloud has demonstrated effectiveness in improving pre-trained model performance across diverse tasks. However, as pre-trained models grow in complexity, fully fine-tuning them for downstream applications demands substantial computational and storage resources. Parameter-efficient fine-tuning (PEFT) methods offer a promising solution to mitigate these resource requirements, yet most current approaches rely on complex adapter and prompt mechanisms that increase tunable parameters. In this paper, we propose PointLoRA, a simple yet effective method that combines low-rank adaptation (LoRA) with multi-scale token selection to efficiently fine-tune point cloud models. Our approach embeds LoRA layers within the most parameter-intensive components of point cloud transformers, reducing the need for tunable parameters while enhancing global feature capture. Additionally, multi-scale token selection extracts critical local information to serve as prompts for downstream fine-tuning, effectively complementing the global context captured by LoRA. The experimental results across various pre-trained models and three challenging public datasets demonstrate that our approach achieves competitive performance with only 3.43% of the trainable parameters, making it highly effective for resource-constrained applications. Source code is available at: https://github.com/songw-zju/PointLoRA. |
| title | PointLoRA: Low-Rank Adaptation with Token Selection for Point Cloud Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.16023 |