PanoCARLA

PanoCARLA is a synthetic panoramic video dataset generated with CARLA 0.9.16 for panoramic understanding tasks. It provides temporally aligned equirectangular RGB images, metric depth annotations, semantic segmentation, and camera poses across multiple CARLA towns and roaming routes.

PanoCARLA has been used for the training of PVDepth.

Dataset Overview

PanoCARLA contains 126,696 frames collectedin eight CARLA towns. The PVDepth protocol uses six towns for training and two disjoint towns for evaluation.

Split	Towns	Routes	Source clips	Frames
Train	Town01, Town03, Town04, Town05, Town06, Town07	54	95	112,988
Test	Town02, Town10	13	22	13,708
Total	8 towns	67	117	126,696

Per-town Statistics

Town	Routes	Clips	Frames
Town01	8	18	15,512
Town02	6	14	6,626
Town03	8	22	17,822
Town04	8	13	23,531
Town05	10	19	25,436
Town06	8	9	15,788
Town07	12	14	14,899
Town10	7	8	7,082

Repository Structure

PanoCARLA/
├── panocarla_h5/                  # Full train/test dataset in HDF5 format (2400 x 1200)
├── town0210_res1024_512.tar       # Raw Town02/Town10 benchmark data (1024 x 512)
├── test_benchmark/                # Benchmark JSON files at different sampling rates
├── setting_dynamic.json           # Full clip and frame index
├── statistics_report_dynamic.txt  # Per-town statistics
├── carla_path_vis/                # Roaming-route visualizations
└── vis_mp4/                       # Panoramic preview videos for all captured routes

The complete dataset is distributed through panocarla_h5/. The raw PNG/NPY representation is provided only for the Town02/Town10 evaluation split in town0210_res1024_512.tar.

setting_dynamic.json indexes all towns and retains paths from the original raw-data layout. For training towns, use the HDF5 files rather than expecting the referenced raw PNG/NPY files to be present.

HDF5 Format

Each file in panocarla_h5/ stores one continuous source clip. The first dimension T is the number of frames in that clip.

Key	Shape	dtype	Description
rgb	[T, 1200, 2400, 3]	uint8	Equirectangular RGB frames
depth	[T, 1200, 2400, 1]	float16	Metric panoramic depth in meters
seg	[T, 1200, 2400, 1]	uint8	CARLA semantic segmentation IDs
poses	[T, 4]	float32	Camera poses in (x, y, z, yaw) order

The position components x, y, and z are expressed in meters in the CARLA world coordinate system, and yaw is expressed in degrees. Pitch and roll are always zero and are therefore not stored.

Semantic IDs follow the official CARLA 0.9.16 semantic segmentation specification.

Reading an HDF5 Clip

import h5py

h5_path = "panocarla_h5/town01_path1_town01_path1_clip_0.h5"

with h5py.File(h5_path, "r") as file:
    rgb = file["rgb"][0]           # [1200, 2400, 3], uint8
    depth = file["depth"][0]       # [1200, 2400, 1], float16, meters
    segmentation = file["seg"][0] # [1200, 2400, 1], uint8
    frame_id = file["frame_ids"][0]
    pose = file["poses"][0]        # x, y, z, yaw

For large clips, read only the required frame indices instead of loading the entire file into memory.

Benchmark Split

Town02 and Town10 form the evaluation split. The raw benchmark data is stored in town0210_res1024_512.tar, and test_benchmark/ provides JSON configurations at 2, 10, and 20 FPS with sequence lengths of 50, 90, and 110 frames, respectively.

Download

This is a large gated dataset. Request access on the dataset page and authenticate with the Hugging Face CLI before downloading:

hf auth login

Download the full HDF5 dataset:

hf download Soon122/PanoCARLA \
  --repo-type dataset \
  --include "panocarla_h5/*" \
  --include "setting_dynamic.json" \
  --local-dir PanoCARLA

Download only the evaluation data:

hf download Soon122/PanoCARLA \
  town0210_res1024_512.tar \
  --repo-type dataset \
  --local-dir PanoCARLA

hf download Soon122/PanoCARLA \
  --repo-type dataset \
  --include "test_benchmark/*" \
  --local-dir PanoCARLA

tar -xf PanoCARLA/town0210_res1024_512.tar -C PanoCARLA

License

PanoCARLA is licensed under CC BY 4.0, which permits commercial use with attribution.

Citation

If you use PanoCARLA in your research, please cite the PVDepth paper:

@inproceedings{song2026pvdepth,
  author    = {Song, Chuanxin and Peng, Peixi},
  title     = {PVDepth: Panoramic Video Depth Estimation via Geometry-Aware Spatiotemporal Adaptation},
  booktitle = {ICML},
  year      = {2026}
}

Acknowledgements

PanoCARLA was generated with CARLA 0.9.16. We thank the CARLA team for making the simulator publicly available.