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license: mit |
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--- |
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# [CVPR 2025] GFS-VL: Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model |
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## Overview |
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GFS-VL is a novel framework proposed in our CVPR 2025 paper: [**Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model**](https://arxiv.org/pdf/2503.16282). |
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Our approach leverages the synergy between: |
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- **Dense but noisy pseudo-labels** from 3D Vision-Language Models |
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- **Precise yet sparse few-shot samples** |
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by maximizing the strengths of both data sources for effective generalized few-shot 3D point cloud segmentation. |
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## Released Model Weights |
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This repository contains the following pre-trained weights: |
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- **PTv3 Backbones**: Our pre-trained point transformer v3 backbones |
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- **GFS-VL Models**: Complete GFS_VL few-shot segmentation framework |
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## Usage |
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For detailed usage instructions, model implementation, and training code, please refer to our [GitHub repository](https://github.com/ZhaochongAn/GFS-VL). |
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## Benchmarks |
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We introduce **two new challenging GFS-PCS benchmarks** with diverse novel classes for comprehensive generalization evaluation. These benchmarks lay a solid foundation for real-world GFS-PCS advancements. |
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The benchmark datasets can be downloaded from our [Huggingface dataset repository](https://huggingface.co/datasets/ZhaochongAn/GFS_PCS_Datasets). |
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## Citation |
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If you find our work useful, please consider citing our paper: |
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```bibtex |
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@inproceedings{an2025generalized, |
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title={Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model}, |
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author={An, Zhaochong and Sun, Guolei and Liu, Yun and Li, Runjia and Han, Junlin and Konukoglu, Ender and Belongie, Serge}, |
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booktitle=CVPR, |
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year={2025} |
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} |
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``` |
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