Canonical Shape Projection is All You Need for 3D Few-shot Class Incremental Learning

Introduction

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.

ECCV 2024 Paper

The paper has been accepted at ECCV 2024. You can find it on the ECCV 2024 website: ECCV 2024

Requirements

• Python (>=3.6)
• PyTorch (>=1.0)

Installation

1. Clone the repository:

   git clone https://github.com/your_username/your_repository.git
   cd your_repository
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

Usage

1. Training: To train the model, run:

   python train.py --options
   

   Make sure to adjust the options and hyperparameters according to your setup.

2. 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.

3. Inference: For inference on new data, use:

   python inference.py --options
   

   Adjust the options as per your requirements for inference tasks.

Citation

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/}
}

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Mention any acknowledgments or credits for libraries, datasets, etc., if applicable.