GAFAR

This repository contains the code necessary to train and evaluate the work presented in

GAFAR: Graph-Attention Feature-Augmentation for Registration
A Fast and Light-weight Point Set Registration Algorithm

a well as to recreate the results stated in the paper.

Training

To train a model download the sub-sampled ModelNet40 point clouds with the official training/testing split from here and unzip it to a location of your choosing. You'll need to update the path to the hdf5 archives in the dataset config files in gafar/config/data accordingly.

To train a model for the respective experiment run e.g.:

python3 matching.py ./config/model/gafar.json ./experiments/unseen_crop/ --dataset ./config/data/unseen_crop.json

Select the dataset config according to the experiment you want to run. All training parameters available can be found in the default configuration in matching.py, all model parameters will be listed in the file config.json in the output directory of the experiment as soon as model training has started.

Validation using the same code as in training can be done with:

python3 matching.py ./config/model/gafar.json ./experiments/unseen_crop/eval/ --model ./weights/gafar_unseen_crop.t7 --dataset ./config/data/unseen_crop.json 

Testing

To recreate the results published in the paper please use the evaluation code adapted from RGM as follows:

python3 evaluate.py --cfg ./config/data/RGM_Unseen_Crop_modelnet40.yaml --model ./config/weights/gafar_crop_unseen.pt --output ./eval/rgm/unseen/ --dataset path/to/ModelNet40/

Please note that changes in batch size, different versions of packages used, differences in GPU driver or NVIDIA CUDA versions (non-deterministic execution) results may vary slightly.

Acknowledgement

In this work, parts or adaptions of the implementations of the following works are used:

• DGCNN
• RPM-Net
• RGM
• SuperGlue

Citation

If you use this code in your work or project, please reference:

@inproceedings{mohr2023gafar
  title={{GAFAR: Graph-Attention Feature-Augmentation for Registration. A Fast and Light-weight Point Set Registration Algorithm}},
  author={{Mohr, Ludwig and Geles, Ismail and Fraundorfer, Friedrich}},
  booktitle={{Proceedings of the 11th European Conference on Mobile Robots ({ECMR})}},
  year={2023}
}