Initial WildCity dataset release

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LICENSE

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+WildCity Dataset License Notice
+
+SPDX-License-Identifier: CC-BY-NC-SA-4.0
+
+Copyright (c) 2026, WildCity Dataset Authors and May Mobility.
+All rights reserved except as expressly granted below.
+
+This dataset is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0), unless otherwise specified.
+
+Official license deed:
+https://creativecommons.org/licenses/by-nc-sa/4.0/
+
+Official legal code:
+https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
+
+Summary of key terms:
+
+1. Attribution
+   You must give appropriate credit, provide a link to the license, and indicate if changes were made.
+
+2. NonCommercial
+   You may not use the dataset for commercial purposes.
+
+3. ShareAlike
+   If you remix, transform, or build upon the dataset and share the resulting material, you must distribute your contributions under the same license as the original.
+
+4. No additional restrictions
+   You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
+
+This notice is provided for convenience. The official CC BY-NC-SA 4.0 legal code governs the license terms.
+
+Recommended citation:
+
+@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}
+}

Miami/2025-01-02_madisonjo_miami/2025-01-02_madisonjo_miami_22k_keyframes.tar

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README.md

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 ---
-license: apache-2.0
+pretty_name: WildCity
+task_categories:
+  - image-to-3d
+  - depth-estimation
+  - object-detection
+tags:
+  - autonomous-driving
+  - 3d-reconstruction
+  - gaussian-splatting
+  - neural-rendering
+  - lidar
+  - rgb
+  - city-scale
+  - simulation
+  - spatial-intelligence
+  - multimodal
 ---
+
+# 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.
+
+```text
+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:
+
+```text
+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:
+
+```text
+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:
+
+```text
+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:
+
+```text
+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:
+
+```bash
+tar -xf ann-arbor/2026-03-02_miranda_ann-arbor/2026-03-02_miranda_ann-arbor_22k_keyframes.tar
+```
+
+or:
+
+```bash
+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:
+
+```text
+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:
+
+```text
+colmap_keyframe_start1k_total10k_all_cams/
+```
+
+In particular:
+
+```text
+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:
+
+```bibtex
+@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:
+
+```text
+xiangyuhan615@gmail.com
+```