Datasets:
File size: 5,465 Bytes
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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.
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