---
license: cc-by-4.0
task_categories:
  - object-detection
  - depth-estimation
  - robotics
tags:
  - autonomous-driving
  - carla
  - bev
  - slam
  - multi-modal
  - lidar
  - camera
  - radar
size_categories:
  - 10K<n<100K
pretty_name: CaScenes
---

# CaScenes

A multi-modal autonomous-driving dataset collected in the CARLA simulator, designed for BEV perception and SLAM research. CaScenes provides time-synchronized camera, LiDAR, radar, GNSS, and IMU streams together with BEV map ground truth, across multiple towns and weather conditions.

## Highlights

- **48,453 keyframes** across **96 segments** in **3 weather conditions** (Sunny, Night, Rainy) in CARLA Town 01.
- **6 surround-view cameras** + **1 top LiDAR** + **5 radars** + **GNSS** + **IMU** + **BEV map** ground truth per frame.
- **Ego pose** (`ego2global`) and full **sensor-to-ego calibration** matrices included.
- Split into `train` (66 segments, 38,768 frames) and `test` (30 segments, 9,685 frames).

## Splits

| Split | Scenarios | Segments | Frames |
|---|---|---|---|
| train | Town01_Sunny / Town01_Night / Town01_Rainy (50 vehicles each) | 22 + 22 + 22 = 66 | 38,768 |
| test  | Town01_Sunny / Town01_Night / Town01_Rainy (50 vehicles each) | 10 + 10 + 10 = 30 | 9,685 |

## Sensors

Each segment contains synchronized per-frame data under the following modalities:

| Modality | Folder | Format | Notes |
|---|---|---|---|
| Front camera | `CAM_FRONT/<ts>.png` | PNG | 800×450 (see `camera_intrinsics`) |
| Front-left camera | `CAM_FRONT_LEFT/<ts>.png` | PNG | |
| Front-right camera | `CAM_FRONT_RIGHT/<ts>.png` | PNG | |
| Back camera | `CAM_BACK/<ts>.png` | PNG | |
| Back-left camera | `CAM_BACK_LEFT/<ts>.png` | PNG | |
| Back-right camera | `CAM_BACK_RIGHT/<ts>.png` | PNG | |
| Top LiDAR | `LIDAR_TOP/<ts>.pcd` | PCD | |
| Radar (×5) | `RADAR_{FRONT,FRONT_LEFT,FRONT_RIGHT,BACK_LEFT,BACK_RIGHT}/<ts>.*` | | |
| GNSS | `GNSS/<ts>.*` | | |
| IMU | `IMU/<ts>.*` | | |
| BEV map (semantic) | `BEV_MAP/<ts>.npy` | NumPy | |
| BEV map (color) | `BEV_MAP_COLOR/<ts>.*` | | |
| BEV view (rendered) | `BEV_VIEW/<ts>.*` | | |
| Ego pose | `VEHICLE_TRANSFORM/<ts>.*` | | Also embedded in `data.json` as `ego2global` |

Each segment also contains a `data.json` index listing every frame with full calibration and file paths.

## `data.json` schema

```jsonc
{
  "infos": [
    {
      "weather": "Sunny",
      "timestamp": 7285258,
      "ego2global":  [[...4×4 row-major...]],   // CARLA world -> ego
      "lidar2ego":   [[...4×4...]],
      "lidar2global":[[...4×4...]],
      "lidar_path":  "CaScenes/datasets/test/.../LIDAR_TOP/26805.pcd",
      "bev_map":     "CaScenes/datasets/test/.../BEV_MAP/26805.npy",
      "cams": {
        "CAM_FRONT": {
          "data_path":         "CaScenes/datasets/test/.../CAM_FRONT/26805.png",
          "lidar2camera":      [[...4×4...]],
          "camera_intrinsics": [[...3×4 / 4×4...]]
        },
        "...": {}
      },
      "radars":  { "...": {} },
      "sweeps":  [ /* nearby unkeyed frames for temporal context */ ]
    }
  ]
}
```

All file paths inside `data.json` are **relative to the parent of the `CaScenes/` directory** — i.e., extract the tarballs from a workspace root and reference files via the paths in `data.json` directly.

## Download & extract

The dataset is distributed as plain (uncompressed) `tar` files. The `train` split is split into ~40 GB chunks (`train.tar.part-aa`, `train.tar.part-ab`, ...) to stay below HF's per-file LFS limit; concatenate them with `cat` before extracting. The `test` split fits in a single `test.tar`.

```bash
# from huggingface_hub (recommended)
pip install -U huggingface_hub
hf download Zixia3/CaScenes \
    --repo-type dataset \
    --local-dir ./CaScenes_release \
    --include "*.tar" "train.tar.part-*" "SHA256SUMS"

cd CaScenes_release
sha256sum -c SHA256SUMS    # verify integrity

# extract into a workspace where `CaScenes/datasets/...` should live
mkdir -p /path/to/workspace && cd /path/to/workspace

# train (split): cat parts back together and pipe into tar
cat /path/to/CaScenes_release/train.tar.part-* | tar -xf -

# test (single tarball)
tar -xf /path/to/CaScenes_release/test.tar

# resulting layout:
#   /path/to/workspace/CaScenes/datasets/train/Town01_Sunny_50_vehicles/segment_0/...
#   /path/to/workspace/CaScenes/datasets/test/Town01_Sunny_50_vehicles/segment_4a/...
```

A minimal Python loader that does download + checksum + extract in one shot is provided in [`download.py`](./download.py).

## License

Released under [Creative Commons Attribution 4.0 International (CC BY 4.0)](./LICENSE). You are free to use, share, and adapt the data, including for commercial purposes, provided you give appropriate credit.

## Credits

CaScenes is collected using the [CARLA](https://carla.org/) open-source autonomous-driving simulator (MIT-licensed). If you use CaScenes, please also credit CARLA:

> Dosovitskiy et al. *CARLA: An Open Urban Driving Simulator.* CoRL 2017.

## Citation

If you use CaScenes in your research, please cite:

```bibtex
@misc{cascenes2026,
  title  = {CaScenes: A Multi-Modal CARLA Dataset for BEV Perception and SLAM},
  author = {Xia, Zixia and others},
  year   = {2026},
  url    = {https://huggingface.co/datasets/Zixia3/CaScenes}
}
```

## Companion code

Methods built on CaScenes live at: <https://github.com/ZixiaXia/SmartFusion-SLAM>

## Changelog

- **v1.0** (2026-05-10) — Initial public release.