This repository contains the implementation for the paper titled "Canonical Shape Projection is All You Need for 3D Few-shot Class Incremental Learning", accepted at ECCV 2024. The goal of this project is to explore and implement the proposed method using Python and PyTorch.
The paper has been accepted at ECCV 2024. You can find it on the ECCV 2024 website: ECCV 2024
- Python (>=3.6)
- PyTorch (>=1.0)
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Clone the repository:
git clone https://github.com/your_username/your_repository.git cd your_repository
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Install the required dependencies:
pip install -r requirements.txt
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Training: To train the model, run:
python train.py --options
Make sure to adjust the options and hyperparameters according to your setup.
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Evaluation: To evaluate the trained model, run:
python evaluate.py --options
Provide the necessary options for evaluation, such as model checkpoint path, evaluation dataset, etc.
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Inference: For inference on new data, use:
python inference.py --options
Adjust the options as per your requirements for inference tasks.
If you find this work useful in your research, please consider citing:
@inproceedings{your_paper_citation,
title={Canonical Shape Projection is All You Need for 3D Few-shot Class Incremental Learning},
author={Your Name and Co-authors},
booktitle={European Conference on Computer Vision (ECCV)},
year={2024},
organization={Springer},
url={https://eccv.ecva.net/}
}
This project is licensed under the MIT License - see the LICENSE file for details.
- Mention any acknowledgments or credits for libraries, datasets, etc., if applicable.