Hugging Face's logo

Datasets:
Neptune615
/
Wild-City

Tasks:
Image-to-3D
Depth Estimation
Object Detection
Tags:
autonomous-driving
3d-reconstruction
gaussian-splatting
neural-rendering
lidar
rgb
Dataset card Files
xet
 Community
Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 83, in _split_generators
                  raise ValueError(
                  ...<2 lines>...
                  )
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 66, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

WildCity Dataset

WildCity is a real-world city-scale multimodal dataset for street-view reconstruction, simulation, and spatial intelligence. It is collected from autonomous-driving fleet logs across multiple U.S. cities and contains surround-view RGB images, LiDAR, calibration, ego and sensor poses, object annotations, semantic masks, and processed reconstruction assets.

This repository hosts the initial public release of WildCity. This version does not include the full raw dataset. Instead, each released sequence archive contains the raw data and processed assets for the first 22k keyframes of the corresponding log.

For details about the dataset, benchmark, and reconstruction pipeline, please refer to our paper:

WildCity: A Real-World City-Scale Testbed for Rendering, Simulation, and Spatial Intelligence
Xiangyu Han, Mengyu Yang, Jiaqi Li, Bowen Chang, Ziyu Chen, Hexu Zhao, Rahul Kumar Agrawal, Anthony Rodriguez, Rajani Acharya, Fiona Hua, Marco Pavone, Chen Feng, Yiming Li
Project page: <project-page-url>
Paper: <paper-url>

Release Status

This is the v0 initial release.

Included in this release:

  • First 22k keyframes for each released sequence.
  • Raw surround-view RGB images.
  • Raw LiDAR data.
  • Per-sensor calibration files.
  • Ego poses and sensor poses.
  • IMU data.
  • Tracked-object annotations.
  • Moving-object bounding-box overlays.
  • Processed COLMAP / 3DGS-ready data.
  • Undistorted images.
  • Semantic masks for reconstruction.
  • LiDAR-camera reprojection depth.
  • COLMAP sparse reconstruction files.

Not included in this release:

  • The complete raw logs.
  • The full WildCity dataset.
  • All city-scale benchmark segments.
  • Pretrained reconstruction models or baseline checkpoints, unless explicitly added in a later release.

Future versions may expand the raw data coverage, processed segments, benchmark splits, and reconstruction outputs.

Repository Structure

The repository is organized by city and sequence. Each sequence is stored as a .tar archive.

repo/
  ann-arbor/
    2026-03-02_miranda_ann-arbor/
      2026-03-02_miranda_ann-arbor_22k_keyframes.tar

  atlanta/
    2026-05-01_m20240024s2_atlanta/
      2026-05-01_m20240024s2_ga-atlanta_22k_keyframes.tar

  ...

Each .tar file contains one sequence folder. For example:

2026-05-01_m20240024s2_ga-atlanta/
  calibration/
  colmap_keyframe_start1k_total10k_all_cams/
  depth_visualization/
  images/
  lidar/
  object_overlays/
  imu.csv
  keyframes_object_bboxes.jsonl
  keyframes_object_centers.jsonl
  keyframes_pose.csv
  keyframes_sensor_poses_local.csv
  keyframes_sensor_poses.csv
  keyframes_tracked_objects.csv
  undistortion_intrinsics.json

Archive Contents

Raw and Metadata Files

calibration/

Contains per-sensor calibration files, including camera intrinsics and sensor extrinsics.

These files define the geometric relationship between cameras, LiDAR, and the ego-vehicle frame.

images/

Contains raw camera images for the released keyframes.

These are the original sensor images before the 3DGS-specific undistortion and preprocessing steps.

lidar/

Contains raw LiDAR data for the released keyframes.

depth_visualization/

Contains depth visualization images.

These files are intended for quick inspection and debugging rather than direct metric evaluation.

object_overlays/

Contains visualizations of moving-object bounding boxes overlaid on images.

These overlays are useful for inspecting dynamic-object annotations and verifying object filtering behavior for reconstruction.

imu.csv

Raw IMU measurements associated with the released sequence.

keyframes_pose.csv

Ego poses for the selected keyframes.

keyframes_sensor_poses.csv

Global sensor poses for the selected keyframes.

keyframes_sensor_poses_local.csv

Local-frame sensor poses for the selected keyframes.

keyframes_tracked_objects.csv

Tracked-object annotations associated with the selected keyframes.

keyframes_object_bboxes.jsonl

Object bounding-box annotations for keyframes.

keyframes_object_centers.jsonl

Object center annotations for keyframes.

undistortion_intrinsics.json

Camera intrinsics used for image undistortion and downstream reconstruction.

3DGS / COLMAP-Ready Processed Data

The processed reconstruction-ready data is stored under:

colmap_keyframe_start1k_total10k_all_cams/

This folder is intended to be directly usable for COLMAP-style and 3D Gaussian Splatting reconstruction pipelines.

Its contents include:

colmap_keyframe_start1k_total10k_all_cams/
  images/
  masks/
    dynamic/
    ground/
    sky/
  depth_map_gt/
  sparse/

colmap_keyframe_start1k_total10k_all_cams/images/

Undistorted images prepared for reconstruction.

These images are the preferred input images for 3DGS training.

colmap_keyframe_start1k_total10k_all_cams/masks/

Semantic masks used by the reconstruction pipeline.

The current release provides three mask types:

masks/
  dynamic/
  ground/
  sky/
  • dynamic/: masks for moving or potentially dynamic objects.
  • ground/: masks for road or ground regions.
  • sky/: masks for sky regions.

These masks are intended for region-aware reconstruction, including moving-object filtering, ground regularization, and sky modeling.

colmap_keyframe_start1k_total10k_all_cams/depth_map_gt/

LiDAR-camera reprojection depth maps.

These provide sparse or semi-dense depth supervision derived from LiDAR projected into camera views.

colmap_keyframe_start1k_total10k_all_cams/sparse/

COLMAP sparse reconstruction files.

This folder contains camera and sparse geometry information in a COLMAP-compatible layout.

Extracting a Sequence

To extract one sequence:

tar -xf ann-arbor/2026-03-02_miranda_ann-arbor/2026-03-02_miranda_ann-arbor_22k_keyframes.tar

or:

tar -xf atlanta/2026-05-01_m20240024s2_atlanta/2026-05-01_m20240024s2_ga-atlanta_22k_keyframes.tar

After extraction, the sequence folder will contain both raw data and processed reconstruction-ready data.

Recommended Usage

Raw-data inspection

Use:

images/
lidar/
calibration/
keyframes_pose.csv
keyframes_sensor_poses.csv
keyframes_tracked_objects.csv

These files are suitable for inspecting raw multimodal sensor data, poses, calibration, and object annotations.

3DGS reconstruction

Use:

colmap_keyframe_start1k_total10k_all_cams/

In particular:

colmap_keyframe_start1k_total10k_all_cams/images/
colmap_keyframe_start1k_total10k_all_cams/masks/
colmap_keyframe_start1k_total10k_all_cams/depth_map_gt/
colmap_keyframe_start1k_total10k_all_cams/sparse/

This processed folder contains undistorted images, semantic masks, LiDAR-projected depth maps, and COLMAP sparse files.

Dataset Versioning

v0

Initial release.

  • Each sequence archive contains the first 22k keyframes.
  • Full raw logs are not included.
  • The release focuses on providing a usable subset with both raw multimodal data and 3DGS-ready processed data.

Notes and Limitations

This initial release is intended to support reproducible research on city-scale reconstruction and neural rendering. It should not be interpreted as the complete WildCity dataset.

Known limitations of this version:

  • Only the first 22k keyframes are included for each released sequence.
  • Some full raw logs and full benchmark segments are not yet released.
  • Semantic masks are automatically generated and may contain boundary errors or rare category mistakes.
  • Poses and calibration are provided for research use, but residual pose noise may still exist in long-horizon real-world logs.

Citation

If you use this dataset, please cite:

@inproceedings{han2026wildcity,
  title     = {WildCity: A Real-World City-Scale Testbed for Rendering, Simulation, and Spatial Intelligence},
  author    = {Han, Xiangyu and Yang, Mengyu and Li, Jiaqi and Chang, Bowen and Chen, Ziyu and Zhao, Hexu and Agrawal, Rahul Kumar and Rodriguez, Anthony and Acharya, Rajani and Hua, Fiona and Pavone, Marco and Feng, Chen and Li, Yiming},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2026}
}

Please update the BibTeX entry according to the official proceedings version.

License

This dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0), unless otherwise specified.

Under this license, you may use, share, and adapt the dataset for non-commercial research and educational purposes, provided that you:

  • give appropriate credit to the WildCity authors;
  • indicate whether changes were made;
  • do not use the dataset for commercial purposes; and
  • distribute any adapted material under the same or a compatible license.

See the LICENSE file in this repository for the license notice and the official Creative Commons legal code.

Contact

For questions about the dataset, please contact:

xiangyuhan615@gmail.com
Downloads last month
9
Total file size:
1.11 TB