This project implements various different deep learning based interest point methods. Right now only superpoint.

• Python >= 3.5
• PyTorch >= 0.4
• tqdm (Optional for test.py)
• tensorboard >= 1.7.0 (Optional for TensorboardX)
• tensorboardX >= 1.2 (Optional for TensorboardX)
• opencv-python

• parmap
• json_minify

For overall project structure see the README in source.

Please modify the configs/magic_point.json. You should set the data_dir as that sets where the generated synthetic shapes are created. Optionally modify the save_dir(where checkpoint files go) and log dir (where log files for tensorboard go).

Then run

python train.py --config configs/magic_point.json

If using this please cite the originally SuperPoint paper

@inproceedings{detone18superpoint,
  author    = {Daniel DeTone and
               Tomasz Malisiewicz and
               Andrew Rabinovich},
  title     = {SuperPoint: Self-Supervised Interest Point Detection and Description},
  booktitle = {CVPR Deep Learning for Visual SLAM Workshop},
  year      = {2018},
  url       = {http://arxiv.org/abs/1712.07629}
}

The Superpoint portions of this project was ported from https://github.com/rpautrat/SuperPoint