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:space_invader: VADER :space_invader:

Paper

Pytorch code for "Generalizable Local Feature Pre-training for Deformable Shape Analysis" - CVPR 2023

You underestimate the power of the local side!


:construction_worker: Installation

pip install -r requirements.txt
python setup.py bdist_wheel
pip install --upgrade dist/diffvoxel-0.0.1-*.whl
python setup.py bdist_wheel
pip install --upgrade dist/pointnet2_ops-3.0.0-*.whl (or the version you have)

:book: Usage

In this repository, we provide the code for pre-training our network to learn local features that can generalizable across different shapes categories, As well as the code for extracting the VADER features used in downstream tasks.

Our paper presents new insights into the transferability of features from networks trained on non-deformable shapes. Once the network is pretrained (we provide pretrained weights), VADER features can be extracted and used as replacements for traditional input features (like XYZ or HKS) in any downstream task.

For all experiments, we adapted the code from Diffusion-Net, by substituting their input features with our VADER features. Visit their repository for detailed usage instructions.

:chart_with_upwards_trend: Results

If you wish to report our result, we have summarized them below. Our method is referred to as VADER. X on Y indicates that the method was trained on dataset X and tested on dataset Y.

:mortar_board: Citation

If you find this work useful in your research, please consider citing:

@inproceedings{attaiki2023vader,
    title={Generalizable Local Feature Pre-training for Deformable Shape Analysis},
    author={Souhaib Attaiki and Lei Li and Maks Ovsjanikov},
    booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2023}
}