EP2P-Loc: End-to-End 3D Point to 2D Pixel Localization for Large-Scale Visual Localization (ICCV 2023)

Official repository of "EP2P-Loc: End-to-End 3D Point to 2D Pixel Localization for Large-Scale Visual Localization".

We propose EP2P-Loc, a novel large-scale visual localization method that mitigates such appearance discrepancy and enables end-to-end training for pose estimation. This repository is built upon the foundations of Swin-Transformer, Fast Point Transformer, and EPro-PnP.

Updates

• Aug 18, 2023: Release benchmark datasets
• Jul 18, 2023: Initial commit

Requirements

• Ubuntu 16.04
• Python 3.6

  
  conda create -n ep2ploc python=3.6
  conda activate ep2ploc

pip install -r requirements.txt conda install -c sirokujira python-pcl --channel conda-forge

## Dataset
### Download datasets
* [2D-3D-S and (Aligned) S3DIS](http://buildingparser.stanford.edu/dataset.html)
* [KITTI](https://www.cvlibs.net/datasets/kitti/eval_odometry.php)

### Preprocessing

cd datasets

2D-3D-S

python preprocess_2d3ds.py --data_path <2D-3D-S_path> --s3dis_path --cache_path <cache_path(optional)> --save_path

KITTI

python preprocess_kitti.py --data_path --save_path

## Training and Testing
TBU

## Citation

@INPROCEEDINGS{EP2PLoc2023ICCV, author = {Kim, Minjung and Koo, Junseo and Kim, Gunhee}, title = {EP2P-Loc: End-to-End 3D Point to 2D Pixel Localization for Large-Scale Visual Localization}, booktitle = {International Conference on Computer Vision (ICCV)}, year = {2023} }