This repository provides the code implementation of our IROS 2025 paer. We developed a Cross-View Deep Front-end Structure-from-Motion System for Sparse Localization in Multi-Altitude Scenes. The performance of our approach is shown in the video.
We also create 2 multi-altitude datasets with ground truth GPS for two different sites. You can download datasets on hugging face: https://huggingface.co/datasets/yaxlee/Stevens-Sky2Ground.
If you find this repository useful, please cite our paper:
We visualize pose estimation results on three different datasets. Yellow Points represent estimated poses; cyan points represent ground truth poses.
Reconstructed Model review on SIT Campus dataset. Red frames represent aerial images, blue frames represent ground images, black frame on the top represent reference satellite image.
Our implementaion is on Ubuntu20.04, python=3.11.5, torch=2.0.0, torchvision=0.15.1.
git clone https://github.com/RobustFieldAutonomyLab/CVD-SfM.git
cd CVD-SfM/colmap
mkdir build && cd build
cmake .. \
-DCMAKE_BUILD_TYPE=Release \
make -j$(nproc)
sudo make install
pip install -r requirements.txt
python run_cvd_sfm.py
Remeber to change the root dir to your own path and make sure /images and /sat dir are included in root dir.
└── Root Dir
├── images
├── image1
├── image2
├── ..
├──sat
├── satellite image
We collect two multi-altitude datasets with ground truth GPS for two different sites. Each contains aerial imagery collected by UAV and ground imagery collected by handheld device. One high-resolution satellite imagery from Google Earth Pro is also included for each site. Ground-level GPS is achieved by RTK GNSS using EMLID Reach RS+ receivers.
This dataset contains 179 aerial images, 186 ground images and 1 satellite image.
This dataset contains 174 aerial images, 139 ground images and 1 satellite image.
Datasets Structure:
└── SIT campus
├── images
├── aerial_image1.png
├── aerial_image2.png
├── ..
├── ground_image1.png
├── ground_image2.png
├── ...
├── gps
├── aerial_image1.json
├── aerial_image2.json
├── ...
├── ground_image1.json
├── ground_image2.json
├── ...
├── satellite_sit.jpg
├── trajectory.jpg
└── Raritan bay
├── ...
Our dataset is available on hugging face: https://huggingface.co/datasets/yaxlee/Stevens-Sky2Ground