wrote README

README.md

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-# scenes-finetuning
+# SCENES: Subpixel Correspondence Estimation With Epipolar Supervision
+
+###  [Paper](https://www.robots.ox.ac.uk/~vgg/publications/2024/Kloepfer24a/kloepfer24a.pdf) | [arXiv](https://arxiv.org/abs/2401.10886)
+<br/>
+
+![before_finetuning](assets/before_finetuning.png)
+![after_finetuning](assets/after_finetuning.png)
+
+## Code Organisation
+
+### Epipolar Losses
+
+We release implementations of the epipolar regression and the epipolar classification losses described in the paper in `epipolar_losses/`. In principle, these should be able to serve as drop-in replacements for regression and classification losses that use ground-truth correspondences, but depending on the model that is fine-tuned some adjustments to the input / output of the functions may need to be made.
+
+### Scripts
+
+#### Bootstrapping: Estimating Fundamental Matrices
+
+In `scripts/estimate_fundamental_matrices.py` we also provide the skeleton of a script to use a pre-trained pixel correspondence estimator to estimate fundamental matrices which can then be used to fine-tune the model using the bootstrapping approach described in the paper. Since the exact form of this script depends quite heavily on the pre-trained model and dataset used, the end-user will need to implement the `setup_dataset` and `setup_model` functions, and likely will need to adjust the format to save the fundamental matrices in.
+
+#### EuRoC-MAV Data Preparation
+
+In `scripts/eurocmav-preparation-scripts`, we provide the scripts that we used to download and pre-process the EuRoC-MAV dataset and to form image pairs. 
+
+First, download the dataset by running `sh scripts/eurocmav-preparation-scripts/download_eurocmav.sh`. 
+Alternatively, you can download the runs directly from [this](https://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets) link.
+
+Then, run `sh scripts/eurocmav-preparation-scripts/prepare_eurocmav.sh` to prepare the dataset with the settings used in the paper.
+
+#### EuRoC-MAV Test Script
+
+The script in `scripts/test_eurocmav.py` evaluates a pixel correspondence estimator on the EuRoC-MAV dataset. The `setup_model` function needs to be implemented before usage.
+This script contains code to compute the metrics we report in the paper.
+It should also be mostly straightforward to adapt this script to compute the same metrics on a different dataset.
+
+## Fine-Tuned Models
+
+Coming Soon! (delayed due to a corrupted disk requiring re-training)
+
+## Citation 
+
+If you find this code useful for your research, please use the following BibTeX entry.
+
+```bibtex
+@article{sun2021loftr,
+  title={SCENES: Subpixel Correspondence Estimation With Epipolar Supervision},
+  author={Kloepfer, Dominik A. and Henriques, Jo\~ao F. and Campbell, Dylan},
+  journal={Proceedings of the International Conference on 3D Vision (3DV)},
+  year={2024},
+  month={mar}
+}
+```
+