Datasets:

Zixia3
/

CaScenes

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

{
  "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.

# 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.

License

Released under Creative Commons Attribution 4.0 International (CC BY 4.0). 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 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:

@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.