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SCENES: Subpixel Correspondence Estimation With Epipolar Supervision

Paper | arXiv


before_finetuning after_finetuning

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.

Note: ASpanFormer, which is one of the models we fine-tune in the paper, also uses a flow loss that requires (coarse) ground-truth correspondences. Similar to the classification loss, we replace those coarse correspondences with the location of the highest-confidence patch that is on the epipolar line as pseudo-ground truth correspondences. For a more convenient adaptation in this and similar cases, we provide a function to compute these pseudo-ground truth correspondences in the epipolar_losses/epipolar_classification_loss.py file.

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 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.

@article{SCENES2024Kloepfer,
  title={SCENES: Subpixel Correspondence Estimation With Epipolar Supervision},
  author={Kloepfer, Dominik A. and Henriques, Jo\~ao F. and Campbell, Dylan},
  inproceedings={Proceedings of the International Conference on 3D Vision (3DV)},
  year={2024},
  month={mar}
}