Evaluation of radial distortion solvers

This repo contains code for paper "Are Minimal Radial Distortion Solvers Necessary for Relative Pose Estimation?" (arxiv: http://arxiv.org/abs/2410.05984, doi: TBA)

Update - PoseLib integration

The best performing solvers including the proposed sampling solvers have been merged into the official PoseLib dev branch.

Installation

Create an environment with pytorch and packaged from requirements.txt.

Install PoseLib fork with robust radial distortion estimators into the environment:

git clone https://github.com/kocurvik/PoseLib
git cd PoseLib
git checkout rd
pip install .

Before running the python scripts make sure that the repo is in your python path (e.g. export PYTHONPATH=/path/to/repo/rd)

GC-RANSAC implementation

Our implementation of the solvers in GC-RANSAC can be found in https://github.com/CharalamposTzamos/radial-distortion-solvers

Running experiments

Datasets

We use four datasets:

• Rotunda and Cathedral - RD Benchmark
• Phototourism - download from the IMC2020 challenge website
• ETH3D - download the multiview undistorted train data from the dataset website.

Extracting matches

You can download the files with the matches.

If you want to extract them yourself you can use prepare_im.py and prepare_bundler.py.

To prepare matches for all datasets you can run:

# generates all matches
python prepare_bundler.py -f superpoint /path/to/stored_matches/rotunda_new /path/to/dataset/rotunda_new
# generates only matches with the same camera in both views
python prepare_bundler.py -f superpoint -e /path/to/stored_matches/rotunda_new /path/to/dataset/rotunda_new

python prepare_bundler.py -n 10000 -f superpoint /path/to/stored_matches/cathedral /path/to/dataset/cathedral
python prepare_bundler.py -n 10000 -f superpoint -e /path/to/stored_matches/cathedral /path/to/dataset/cathedral

python prepare_im.py -f superpoint /path/to/stored_matches/ETH3D/multiview_undistorted /path/to/dataset/ETH3D/multiview_undistorted
python prepare_im.py -f superpoint -n 5000 /path/to/stored_matches/phototourism /path/to/dataset/phototourism/

You can change the features to SIFT by setting -f sift. You can choose any /path/to/stored_matches/ it can also be the same as the original dataset directory.

Running experiments

To run the experiments you should modify experiments.sh as described in the script comments and run it.

Tables and figures

Tables and figures are generated using utils/tables.py, utils/tables_real.py and utils/vis.py.

Citation

TBA