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weights release

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README.md ADDED
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+ ---
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+ license: mit
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+ ---
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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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+
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+ ## Overview
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+
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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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+
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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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+
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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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+
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+ ## Released Model Weights
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+
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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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+
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+ ## Usage
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+
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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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+
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+ ## Benchmarks
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+
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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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+
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+ The benchmark datasets can be downloaded from our [Huggingface dataset repository](https://github.com/ZhaochongAn/GFS-VL).
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+
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+ ## Citation
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+
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+ If you find our work useful, please consider citing our paper:
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+
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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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