Release 0.1.0: A9 r02_s01 HDF5 caches (a9_r02_s01_*)

README.md

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+---
+license: cc-by-nc-nd-4.0
+pretty_name: xcalib A9 r02_s01 HDF5 caches
+tags:
+- camera-lidar
+- cross-modal-matching
+- extrinsic-calibration
+- targetless-calibration
+- hdf5
+- a9
+- tum-traffic
+---
+
+# xcalib A9 r02_s01 HDF5 caches
+
+Precomputed matching caches for **camera-LiDAR cross-modal matching** and **targetless extrinsic calibration**, built from the TUM Traffic / A9 `s110` intersection recording. Each HDF5 file stores per-frame camera images, LiDAR point clouds, object detections, and the camera-LiDAR match matrices used to train and evaluate the `xcalib` matcher front-end.
+
+This dataset repo is public and accompanies the accepted xcalib paper.
+
+## Dataset details
+
+- **Task:** associate 2D camera detections with 3D LiDAR detections without calibration targets, then recover the camera-LiDAR extrinsic `[R|t]` (`P = K [R|t]`) from confident matches.
+- **Source:** derived from the **TUM Traffic / A9** dataset (`s110` intersection).
+- **Sensors (per frame):** two Basler cameras (`s110_camera_basler_south1_8mm`, `s110_camera_basler_south2_8mm`) and one LiDAR. Images are JPEG-encoded (decoded BGR to RGB); point clouds are XYZ in **meters**, in the same coordinate frame as the 3D boxes. Camera intrinsics are assumed known; `xcalib` solves only the extrinsic `[R|t]`.
+
+## Splits
+
+- `a9_r02_s01_train.h5` - training
+- `a9_r02_s01_val.h5` - validation
+- `a9_r02_s01_test.h5` - held-out test (29 frames; never seen during training)
+
+## HDF5 schema
+
+| path | dtype | description |
+|---|---|---|
+| `images/<camera>/data` | uint8 (JPEG bytes) `[N]` | encoded RGB frames, one per frame |
+| `point_clouds/<lidar>/<frame>/xyz` | float32 `[P, 3]` | LiDAR points, meters |
+| `labels/<sensor>/<frame>/num_camera_detections` | int | number of 2D boxes `K` |
+| `labels/<sensor>/<frame>/num_lidar_detections` | int | number of 3D boxes `M` |
+| `labels/<sensor>/<frame>/camera_bbox_2d` | float32 `[K, 4]` | 2D boxes `(x1, y1, x2, y2)` in pixels |
+| `labels/<sensor>/<frame>/lidar_bbox_3d` | float32 `[M, 6]` | 3D LiDAR boxes (6 values) |
+| `labels/<sensor>/<frame>/match_matrix` | uint8 `[K, M]` | camera-LiDAR match labels |
+| `labels/<sensor>/<frame>/camera_names` | bytes `[K]` | per-detection source camera (optional) |
+| `calibration` | n/a | optional; absent in matching-only caches |
+
+Bounding-box arrays are allocated with capacity and sliced by the `num_*` counts (`[0:K)` / `[0:M)`).
+
+## Usage
+
+```python
+from xcalib import load_dataset
+
+loader = load_dataset("a9_dataset_r02_s01", split="test")
+frame = loader[0]   # .image / .point_cloud / .bboxes_2d / .bboxes_3d / .match_matrix
+```
+
+Install with `pip install xcalib`; the loader fetches the cache from the Hub on first use and caches it locally.
+
+## Files
+
+| split | file | SHA-256 |
+|---|---|---|
+| `train` | `a9_r02_s01_train.h5` | `1b98298911fcd9e64f8e1651d7a8165c23f9e4df74fe957cec4caf734c13ab09` |
+| `val` | `a9_r02_s01_val.h5` | `ba724986d6aef62e5834de97954098d4d3d8c0620bbb2ba82d271301f45ffbfa` |
+| `test` | `a9_r02_s01_test.h5` | `bab6e7527682549bd3c01c3f0630365692f1e8906361d19fa5842bf0393739de` |
+
+## License & upstream dataset
+
+Cache files are released under **CC BY-NC-ND 4.0**. They derive from the **TUM Traffic / A9** dataset; users must follow the upstream dataset's license terms. The `xcalib` source code is Apache-2.0.
+
+## Upstream citation
+
+If you use these caches, please also cite the upstream A9 Intersection Dataset:
+
+```bibtex
+@inproceedings{zimmer2023a9intersection,
+  title     = {A9 Intersection Dataset: All You Need for Urban 3D Camera-LiDAR Roadside Perception},
+  author    = {Zimmer, Walter and Cre{\ss}, Christian and Nguyen, Hieu and Knoll, Alois C.},
+  booktitle = {2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)},
+  year      = {2023},
+  note      = {arXiv:2306.09266},
+}
+```
+
+## Authors
+
+Lihao Guo, Jiahao Tang, Tam Bang, Tianya Zhang, Austin Harris, Mina Sartipi, Siyang Cao
+
+## Citation
+
+```bibtex
+@article{guo2026xcalib,
+  author  = {Guo, Lihao and Tang, Jiahao and Bang, Tam and Zhang, Tianya and
+             Harris, Austin and Sartipi, Mina and Cao, Siyang},
+  title   = {Position Encoding in Detection-Based LiDAR--Camera Matching:
+             A Diagnostic Study at Infrastructure Sites},
+  journal = {IEEE Sensors Letters},
+  year    = {2026},
+  note    = {Accepted. Paper URL pending. Code:
+             https://github.com/radar-lab/xcalib},
+}
+```

a9_r02_s01_test.h5

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a9_r02_s01_train.h5

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a9_r02_s01_val.h5

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