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Datasets:
ori-drs
/
oxford_spires_dataset

Tasks:
Robotics
Image-to-3D
ArXiv:
Tags:
slam
lidar
3d-reconstruction
nerf
3d-gaussian-splatting
localization
License:
Dataset card Files
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label training_data
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2674, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2208, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2241, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2197, in cast_table_to_features
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1795, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1995, in cast_array_to_feature
                  return feature.cast_storage(array)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1172, in cast_storage
                  [self._strval2int(label) if label is not None else None for label in storage.to_pylist()]
                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1101, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label training_data

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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.

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.

python scripts/dataset_download.py

Install Python Tools

Install oxspires_tools to access Python utilities for using the dataset:

pip install .

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

BUILD_CPP=1 pip install .

Alternatively, use the provided Docker container:

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:

python scripts/generate_depth.py

Citation

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

@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},
}
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