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--- |
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license: cc-by-nc-sa-4.0 |
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task_categories: |
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- robotics |
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- image-to-3d |
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tags: |
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- slam |
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- lidar |
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- 3d-reconstruction |
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- nerf |
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- 3d-gaussian-splatting |
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- localization |
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- sfm |
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- mvs |
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- multimodal |
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- oxford |
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--- |
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We present the Oxford Spires Dataset, captured in and around well-known landmarks in Oxford using a custom-built multi-sensor perception unit as well as a millimetre-accurate map from a terrestrial LiDAR scanner (TLS). The perception unit includes three global shutter colour cameras, an automotive 3D LiDAR scanner, and an inertial sensor — all precisely calibrated. |
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- [Project page](https://dynamic.robots.ox.ac.uk/datasets/oxford-spires/) |
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- [Paper](https://huggingface.co/papers/2411.10546) |
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- [Arxiv](https://arxiv.org/abs/2411.10546) |
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- [Video](https://youtu.be/AKZ-YrOob_4?si=rY94Gn96V2zfQBNH) |
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- [Code](https://github.com/ori-drs/oxford_spires_dataset) |
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### Sample Usage |
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### Download the Dataset |
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You can download the dataset from Hugging Face using the provided script. You can specify which folders to download by changing the `example_pattern`. Core sequences are also defined in the script. |
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```bash |
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python scripts/dataset_download.py |
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``` |
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### Install Python Tools |
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Install `oxspires_tools` to access Python utilities for using the dataset: |
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```bash |
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pip install . |
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``` |
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To enable C++/Python bindings (requires PCL and Octomap): |
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```bash |
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BUILD_CPP=1 pip install . |
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``` |
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Alternatively, use the provided Docker container: |
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```bash |
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docker compose -f .docker/oxspires/docker-compose.yml run --build oxspires_utils |
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``` |
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### Generate Depth Images |
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The following script downloads synchronised images and LiDAR data from a sequence on Hugging Face and generates depth images, LiDAR overlaid on camera images, and surface normal images: |
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```bash |
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python scripts/generate_depth.py |
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``` |
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### Citation |
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If you use The Oxford Spires Dataset in your research, please cite the following paper: |
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```bibtex |
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@article{tao2025spires, |
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title={The Oxford Spires Dataset: Benchmarking Large-Scale LiDAR-Visual Localisation, Reconstruction and Radiance Field Methods}, |
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author={Tao, Yifu and Mu{\~n}oz-Ba{\~n}{\'o}n, Miguel {\'A}ngel and Zhang, Lintong and Wang, Jiahao and Fu, Lanke Frank Tarimo and Fallon, Maurice}, |
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journal={International Journal of Robotics Research}, |
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year={2025}, |
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} |
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``` |
