---
license: cc-by-nc-sa-4.0
task_categories:
- robotics
- image-to-3d
tags:
- slam
- lidar
- 3d-reconstruction
- nerf
- 3d-gaussian-splatting
- localization
- sfm
- mvs
- multimodal
- oxford
---

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.
- [Project page](https://dynamic.robots.ox.ac.uk/datasets/oxford-spires/)
- [Paper](https://huggingface.co/papers/2411.10546)
- [Arxiv](https://arxiv.org/abs/2411.10546)
- [Video](https://youtu.be/AKZ-YrOob_4?si=rY94Gn96V2zfQBNH)
- [Code](https://github.com/ori-drs/oxford_spires_dataset)

### Sample Usage

### Download the Dataset

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.

```bash
python scripts/dataset_download.py
```

### Install Python Tools

Install `oxspires_tools` to access Python utilities for using the dataset:

```bash
pip install .
```

To enable C++/Python bindings (requires PCL and Octomap):

```bash
BUILD_CPP=1 pip install .
```

Alternatively, use the provided Docker container:

```bash
docker compose -f .docker/oxspires/docker-compose.yml run --build oxspires_utils
```

### Generate Depth Images

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:

```bash
python scripts/generate_depth.py
```

### Citation

If you use The Oxford Spires Dataset in your research, please cite the following paper:

```bibtex
@article{tao2025spires,
title={The Oxford Spires Dataset: Benchmarking Large-Scale LiDAR-Visual Localisation, Reconstruction and Radiance Field Methods},
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},
journal={International Journal of Robotics Research}, 
year={2025},
}
```