This repository contains CrossLoc Benchmark Datasets setup and splitting scripts. Please make sure you have access to the CrossLoc Benchmark Raw Datasets before proceeding. The raw datasets could be found as follows:
oneDriveGoogle DriveDryad(Full CrossLoc Benchmark Datasets only)
We present the Urbanscape and Naturescape datasets, each consiting of multi-modal synthetic data and real images with accurate geo-tags captured by drone. See below for a preview!
Ideally, you may want to use this repository as the starting template of your own project, as the python dependency, dataset and basic dataloader have already been developed.
Happy coding! :)
- 3D textured models used to render the benchmark datasets. The dots and boxes denote the camera position distribution. Please check the paper for details!
The CrossLoc Benchmark datasets are officially presented in the paper accepted to CVPR 2022
CrossLoc: Scalable Aerial Localization Assisted by Multimodal Synthetic Data
Qi Yan, Jianhao Zheng, Simon Reding, Shanci Li, Iordan Doytchinov
École Polytechnique Fédérale de Lausanne (EPFL)
Links: website | arXiv | code repos | datasets
- If
condaenvironment is available:
conda env create -f setup/environment.yml
conda activate crossloc- Otherwise, if
condaenvironment is not readily available:
python3 -m venv venvcrossloc
source venvcrossloc/bin/activate
pip3 install pip -U && pip3 install -r setup/requirements.txtopen3d==0.9.0 may raise an error at some environment. You may remove the version limit to proceed.
- Setup datasets: we adopt some DSAC* resources and keep its dataset convention.
cd datasets
echo $DATA_DIR # point to the CrossLoc Benchmark Raw Datasets
export OUT_DIR=$(pwd)/urbanscape
python setup_urbanscape.py --dataset_dir $DATA_DIR/urbanscape --output_dir $OUT_DIR
export OUT_DIR=$(pwd)/naturescape
python setup_naturescape.py --dataset_dir $DATA_DIR/naturescape --output_dir $OUT_DIRPlease note that all RGB images are linked to the DATA_DIR directory using symbolic link.
- When you have the raw dataset locally preserved, please don't move it elsewhere after setting up. Otherwise, the symbolic links to raw RGB images don't work.
- Use
--ignore_3d_labelflag to omit 3D label generation such as coordinate, depth and normal, i.e.,python setup_urbanscape.py --dataset_dir $DATA_DIR --output_dir $OUT_DIR --ignore_3d_label. In this case, only lightweightcalibration,poses,rgb, andsemanticsfolders will be generated (see next section for what they exactly mean).
After setting up the dataset successfully, you would see such directory structure:
├── test_drone_real # drone-trajectory **in-place** real data, for testing
│ ├── calibration # focal length
│ ├── depth # (possibly) down-sampled z-buffer depth
│ ├── init # (possibly) down-sampled scene coordiante
│ ├── normal # (possibly) down-sampled surface normal
│ ├── poses # 4x4 homogeneous cam-to-world transformation matrix
│ ├── rgb # RGB image (symbolic link)
│ └── semantics # (possibly) full-size semantics map
├── test_drone_sim # drone-trajectory **in-place** equivalent synthetic data, for testing
├── test_oop_drone_real # drone-trajectory **out-of-place** real data, for testing
├── test_oop_drone_sim # drone-trajectory **out-of-place** equivalent synthetic data, for testing
├── train_drone_real # drone-trajectory **in-place** real data, for training
├── train_drone_sim # drone-trajectory **in-place** equivalent synthetic data, for training
├── train_oop_drone_real # drone-trajectory **out-of-place** real data, for training
├── train_oop_drone_sim # drone-trajectory **out-of-place** equivalent synthetic data, for training
├── train_sim # LHS synthetic data, for training
├── train_sim_plus_drone_sim # combination of LHS and drone-trajectory **in-place** synthetic data, for training
├── train_sim_plus_oop_drone_sim # combination of LHS and drone-trajectory **out-of-place** synthetic data, for training
├── val_drone_real # drone-trajectory **in-place** real data, for validation
├── val_drone_sim # drone-trajectory **in-place** equivalent synthetic data, for validation
├── val_oop_drone_real # drone-trajectory **out-of-place** real data, for validation
├── val_oop_drone_sim # drone-trajectory **out-of-place** equivalent synthetic data, for validation
└── val_sim # LHS synthetic data, for validationAll directories have the same sub-folders (calibration, depth, init and others). To make the folder tree concise, only the sub-folders in the very first directory test_drone_sim is shown.
We randomly split the In-place and Out-of-place scene data into training (40%), validation (10%) and testing (50%) sections. As for the LHS-sim scene data, it is split into training (90%) and validation (10%) sets. We intentionally formulate a challenging visual localization task by using more real data for testing than for training to better study the real data scarcity mitigation.
See dataset_rollout.ipynb to have a preview on the dataset!
See dataset_statistics.ipynb to compute some statistics of the dataset.
If you find our code useful for your research, please cite the paper:
@article{yan2021crossloc,
title={CrossLoc: Scalable Aerial Localization Assisted by Multimodal Synthetic Data},
author={Yan, Qi and Zheng, Jianhao and Reding, Simon and Li, Shanci and Doytchinov, Iordan},
journal={arXiv preprint arXiv:2112.09081},
year={2021}
}@misc{iordan2022crossloc,
title={CrossLoc Benchmark Datasets},
author={Doytchinov, Iordan and Yan, Qi and Zheng, Jianhao and Reding, Simon and Li, Shanci},
publisher={Dryad},
doi={10.5061/DRYAD.MGQNK991C},
url={http://datadryad.org/stash/dataset/doi:10.5061/dryad.mgqnk991c},
year={2022}
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent