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georg-bn authored Dec 1, 2023
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A steerer is a linear map that modifies keypoint descriptions as if they were obtained from a rotated image. More info [below](#short-summary).

<img src="example_images/method.png" width="400">
<img src="example_images/method.png" width="500">

See the example notebook [demo.ipynb](demo.ipynb) for simple matching examples.
Before running it, create a new virtual environment of your preference (e.g. conda) with `python>=3.9`, `jupyter notebook` and GPU-enabled PyTorch.
Then install the `rotation_steerers` package using pip (this automatically installs DeDoDe from GitHub as well, see `setup.py`):
```
pip install .
```
To download model weights needed for the demo and put them in a new folder `model_weights`, run
The weights are uploaded to [releases](https://github.com/georg-bn/rotation-steerers/releases). To download model weights needed for the demo and put them in a new folder `model_weights`, run
```
bash download_weights.sh
```
The weights are uploaded to [releases](https://github.com/georg-bn/rotation-steerers/releases).

We will publish training code and further model weights shortly.

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Here we provide the gif version of Figure 4 in the paper. We show how the eigenvalues of the steerer evolve in the complex plane during training when we train the steerer and descriptor jointly. The first three correspond to a C4-steerer and the last two show the Lie algebra generator of an SO(2)-steerer.

<img src="eigen_gifs/C4_standard_init.gif" width="300"><img src="eigen_gifs/C4_perm_init.gif" width="300"><img src="eigen_gifs/C4_inv_init.gif" width="300"><img src="eigen_gifs/SO2_standard_init.gif" width="300"><img src="eigen_gifs/SO2_spread_init.gif" width="300">
<img src="eigen_gifs/C4_standard_init.gif" width="250"><img src="eigen_gifs/C4_perm_init.gif" width="250"><img src="eigen_gifs/C4_inv_init.gif" width="250"><img src="eigen_gifs/SO2_standard_init.gif" width="250"><img src="eigen_gifs/SO2_spread_init.gif" width="250">

## Citation
```
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