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+

README.md

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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.

SHA256SUMS

@@ -0,0 +1,4 @@
+551a2a27f481798ca74811e465aa1756db77053e5e4511d9a3840c0e93808eab  test.tar
+3fa0ec24b55d0e184027a24a3782a02ab4ef7978d07ad0673c3b0a7d892d1c21  train.tar.part-aa
+720f427f4e6fb76d62a042644d7df0d72a2acd5c087031c83ef88f21a81bf71f  train.tar.part-ab
+84371faea0b686ac6fa43f9b1054d7dced5ab18804b3724223e8907c34caaba7  train.tar.part-ac

download.py

@@ -0,0 +1,126 @@
+"""Download and extract CaScenes from the Hugging Face Hub.
+
+Usage:
+    python download.py --out ./workspace
+    python download.py --out ./workspace --split test       # download only test
+    python download.py --out ./workspace --no-extract       # download tarballs only
+
+Requirements:
+    pip install -U huggingface_hub
+"""
+
+import argparse
+import hashlib
+import subprocess
+import sys
+from pathlib import Path
+
+from huggingface_hub import HfApi, hf_hub_download
+
+REPO_ID = "Zixia3/CaScenes"
+REPO_TYPE = "dataset"
+SPLITS = ("train", "test")
+
+
+def sha256_of(path: Path, chunk: int = 1 << 20) -> str:
+    h = hashlib.sha256()
+    with path.open("rb") as f:
+        for buf in iter(lambda: f.read(chunk), b""):
+            h.update(buf)
+    return h.hexdigest()
+
+
+def load_expected_sums(sumfile: Path) -> dict[str, str]:
+    sums: dict[str, str] = {}
+    for line in sumfile.read_text().splitlines():
+        line = line.strip()
+        if not line or line.startswith("#"):
+            continue
+        digest, _, name = line.partition("  ")
+        sums[name.strip()] = digest.strip()
+    return sums
+
+
+def verify(path: Path, expected: str) -> None:
+    print(f"  verifying {path.name} ...", flush=True)
+    got = sha256_of(path)
+    if got != expected:
+        raise SystemExit(
+            f"sha256 mismatch for {path.name}:\n  expected {expected}\n  got      {got}"
+        )
+    print(f"  {path.name}: OK")
+
+
+def list_split_files(split: str) -> list[str]:
+    """Return remote filenames belonging to a split, sorted in concat order."""
+    api = HfApi()
+    files = api.list_repo_files(REPO_ID, repo_type=REPO_TYPE)
+    if split == "train":
+        # multi-part: train.tar.part-aa, train.tar.part-ab, ...
+        parts = sorted(f for f in files if f.startswith("train.tar.part-"))
+        if parts:
+            return parts
+        # fallback: single train.tar (if release ever switches back)
+        if "train.tar" in files:
+            return ["train.tar"]
+        raise SystemExit("no train.tar.part-* or train.tar found in repo")
+    if split == "test":
+        if "test.tar" in files:
+            return ["test.tar"]
+        raise SystemExit("test.tar not found in repo")
+    raise ValueError(split)
+
+
+def extract_split(split: str, local_files: list[Path], out_dir: Path) -> None:
+    out_dir.mkdir(parents=True, exist_ok=True)
+    print(f"  extracting {split} ({len(local_files)} file(s)) -> {out_dir} ...", flush=True)
+    if len(local_files) == 1:
+        rc = subprocess.call(["tar", "-xf", str(local_files[0]), "-C", str(out_dir)])
+        if rc != 0:
+            raise SystemExit(f"tar -xf failed for {local_files[0].name}")
+        return
+    # multi-part: cat parts | tar -xf -
+    cat = subprocess.Popen(["cat", *map(str, local_files)], stdout=subprocess.PIPE)
+    tar = subprocess.Popen(["tar", "-xf", "-", "-C", str(out_dir)], stdin=cat.stdout)
+    if cat.stdout is not None:
+        cat.stdout.close()
+    tar_rc = tar.wait()
+    cat.wait()
+    if cat.returncode != 0 or tar_rc != 0:
+        raise SystemExit(f"extraction failed (cat={cat.returncode}, tar={tar_rc})")
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(description="Download & extract CaScenes")
+    ap.add_argument("--out", required=True, type=Path, help="workspace root; CaScenes/ will land inside")
+    ap.add_argument("--split", choices=SPLITS, action="append",
+                    help="restrict to these splits (repeatable). Default: both.")
+    ap.add_argument("--cache", type=Path, default=None,
+                    help="huggingface_hub cache dir (default: HF_HOME or ~/.cache/huggingface)")
+    ap.add_argument("--no-extract", action="store_true", help="download tarballs only, skip extraction")
+    args = ap.parse_args()
+
+    splits = tuple(args.split) if args.split else SPLITS
+    args.out.mkdir(parents=True, exist_ok=True)
+
+    sums_path = Path(hf_hub_download(REPO_ID, "SHA256SUMS", repo_type=REPO_TYPE, cache_dir=args.cache))
+    expected = load_expected_sums(sums_path)
+
+    for split in splits:
+        remote_files = list_split_files(split)
+        local_files: list[Path] = []
+        for rf in remote_files:
+            local = Path(hf_hub_download(REPO_ID, rf, repo_type=REPO_TYPE, cache_dir=args.cache))
+            if rf in expected:
+                verify(local, expected[rf])
+            else:
+                print(f"warning: {rf} not listed in SHA256SUMS; skipping verification", file=sys.stderr)
+            local_files.append(local)
+        if not args.no_extract:
+            extract_split(split, local_files, args.out)
+
+    print("done.")
+
+
+if __name__ == "__main__":
+    main()

test.tar

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+oid sha256:551a2a27f481798ca74811e465aa1756db77053e5e4511d9a3840c0e93808eab
+size 28851015680

train.tar.part-aa

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+oid sha256:3fa0ec24b55d0e184027a24a3782a02ab4ef7978d07ad0673c3b0a7d892d1c21
+size 42949672960

train.tar.part-ab

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+size 42949672960

train.tar.part-ac

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+size 30302556160