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README.md

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+---
+language:
+- en
+pretty_name: WideDepth train
+task_categories:
+- depth-estimation
+task_ids:
+- stereo-depth-estimation
+tags:
+- autonomous-driving
+- fisheye
+- equirectangular
+- lidar
+- disparity
+- depth-estimation
+size_categories:
+- 10K<n<100K
+license: cc-by-nc-4.0
+---
+
+# WideDepth train
+
+## Dataset Summary
+WideDepth train is an outdoor multi-view fisheye training dataset for depth/disparity learning and domain adaptation.
+
+The dataset accompanies the paper *WideDepth: Millimeter-Accurate Benchmark for Fisheye Depth Estimation*, accepted to ICRA 2026.
+
+It provides synchronized frames across:
+- left fisheye RGB
+- right fisheye RGB
+- upper equirect RGB
+- lower virtual equirect RGB
+- equirect disparity
+- fisheye depth
+
+This subset was captured outdoors with a handheld rig (fisheye stereo + LiDAR), then rectified/warped to equirect views and paired with sparse LiDAR-derived labels.
+
+## Dataset Structure
+Expected layout:
+
+```text
+WideDepth_train/
+  left_fisheye/
+  right_fisheye/
+  equirect_up/
+  equirect_down_virt/
+  disp_equirect/
+  depth_fisheye/
+  manifests/
+```
+
+All modality folders use synchronized zero-padded numeric names (for example `00000.png`, `00001.png`, ...).
+
+Content by folder:
+- `left_fisheye`, `right_fisheye`, `equirect_up`, `equirect_down_virt`: RGB images (`uint8`, 3-channel)
+- `disp_equirect`: disparity map image files (typically scaled by `100`)
+- `depth_fisheye`: projected sparse depth map image files in left fisheye frame (typically depth in `mm`)
+- `manifests`: filtering/reindexing outputs and mapping files
+
+## Source Data and Processing
+High-level pipeline:
+1. Outdoor handheld capture with a synchronized fisheye stereo + LiDAR rig.
+2. Stereo rectification and warping to equirectangular views.
+3. Projection of merged LiDAR points to the camera frame to generate sparse depth and derived disparity targets.
+4. Release preparation with optional RGB anonymization (face/plate blur) and synchronized frame indexing across modalities.
+
+Projection model:
+- Equirectangular warping and LiDAR-to-image projection are performed with the **Double Sphere** camera model using calibrated intrinsics/extrinsics.
+
+Capture setup (train release):
+- Two ZED X One fisheye cameras in a vertical stereo configuration.
+- Camera lens FoV is approximately 180° horizontal and 120° vertical; calibration was done at `1920x1080`.
+- Two Livox Mid-360 LiDARs with partially overlapping FoVs.
+- Typical sensor rates: cameras at 30 Hz and LiDARs at 10 Hz, aligned by timestamp in ROS.
+- Data was captured outdoors (city areas, parks, streets), daylight, mostly cloudy conditions.
+
+Notes:
+- Reported train-set sparse projected depth density is approximately 8.1%.
+- This release is intended as training data; create your own deterministic train/val/test split if needed.
+
+## Limitations and Privacy
+- `depth_fisheye` is sparse and can be noisy, especially on thin/far structures.
+- Depth overlays can show local drift due to sparsity, timing offsets, and calibration residuals.
+- Disparity/depth are image-encoded and require proper scaling in loaders.
+
+Anonymization in this release is **best-effort**:
+- All published RGB images in this release were processed with automated face/plate blurring.
+- Automated blur reduces identifiable content but may miss cases.
+- Verify privacy compliance for your jurisdiction and use case before redistribution/deployment.
+
+## Links
+- Paper: https://arxiv.org/abs/2605.24074
+- WideDepth benchmark dataset: https://huggingface.co/datasets/IlyaInd/WideDepth
+- WideDepth code repository: https://github.com/IlyaInd/WideDepth
+
+## License
+This release uses **Creative Commons Attribution-NonCommercial 4.0 International** (`cc-by-nc-4.0`).
+
+You must provide attribution and may not use the dataset for commercial purposes.
+
+## Citation
+```bibtex
+@article{indyk2026widedepth,
+  title={WideDepth: Millimeter-Accurate Benchmark for Fisheye Depth Estimation},
+  author={Indyk, Ilia and Penshin, Ignat and Sosin, Ivan and Monastyrny, Maxim and Valenkov, Aleksei and Makarov, Ilya},
+  journal={arXiv preprint arXiv:2605.24074},
+  year={2026}
+}
+```