TLC-Calib Processed Dataset

This repository provides scene-level processed data in the format used by Targetless LiDAR-Camera Calibration with Neural Gaussian Splatting (TLC-Calib).

TLC-Calib Processed Dataset

• ArXiv: https://arxiv.org/abs/2504.04597
• Paper: https://doi.org/10.1109/LRA.2026.3665066
• Code: https://github.com/SNU-VGILab/TLC-Calib
• Waymo preprocessing: https://github.com/zang09/TLC-Calib_preprocessing

The data files are distributed as scene-level ZIP archives. The Hugging Face Dataset Viewer shows only the scene manifest in metadata/scenes.csv; download the archives as files to run TLC-Calib.

Availability

Source dataset	Released content	Number of scenes	Notes
KITTI-360	Processed scene archives	5	Subject to the original KITTI-360 terms; see LICENSE and THIRD_PARTY_NOTICES.md.
FAST-LIVO2	Processed scene archives	3	Publish only after confirming a dataset redistribution license or written permission from the source-data owner.
Waymo Open Dataset	Configuration and reproduction instructions only	3	No Waymo-derived data is redistributed here.

Repository Layout

TLC-Calib/
├── FAST-LIVO2/
│   ├── README.md
│   ├── Building.zip
│   ├── Landmark.zip
│   └── Sculpture.zip
├── KITTI-360/
│   ├── README.md
│   ├── large_rotation.zip
│   ├── large_zigzag.zip
│   ├── small_rotation.zip
│   ├── small_zigzag.zip
│   └── straight.zip
├── Waymo/
│   └── README.md
├── metadata/
│   └── scenes.csv
├── CONFIG.md
├── LICENSE
├── THIRD_PARTY_NOTICES.md
└── README.md

Download

Install the current Hugging Face Hub client and download the repository:

python -m pip install -U huggingface_hub
hf download b1o1o1m/TLC-Calib \
  --repo-type dataset \
  --local-dir ./TLC-Calib

To download one archive only:

hf download b1o1o1m/TLC-Calib \
  KITTI-360/large_rotation.zip \
  --repo-type dataset \
  --local-dir ./TLC-Calib

Extract the Released Archives

Run the following command from the repository root:

find FAST-LIVO2 KITTI-360 \
  -type f -name '*.zip' \
  -execdir unzip -n '{}' \;

Each archive should contain exactly one top-level scene directory. For example:

KITTI-360/large_rotation.zip
└── large_rotation/
    ├── images/
    ├── lidar/
    ├── params/
    ├── pcds/
    ├── README.md
    └── valid_frame.txt

After extraction, a TLC-Calib scene follows this layout:

<scene_name>/
├── images/
│   ├── image_00/
│   ├── image_01/
│   ├── ...
│   └── image_XX/
├── lidar/
│   ├── map.ply
│   └── rgb_map.ply
├── params/
├── pcds/
├── README.md
└── valid_frame.txt

image_00 to image_XX are placeholders. The number of camera folders depends on the source dataset.

Contents

• images/: synchronized camera images indexed by zero-based local frame ID.
• lidar/: aggregated scene-level LiDAR maps, including rgb_map.ply (colorized) and map.ply (without color).
• params/: camera intrinsics, per-frame poses, camera-to-LiDAR extrinsics, ground-truth and initialization extrinsics, and optional LiDAR timestamps.
• pcds/: per-frame LiDAR point clouds in binary PCD format.
• README.md: scene-specific source and frame-range information.
• valid_frame.txt: mapping from each local frame index to the corresponding original frame ID.

Conventions

• All modalities use the same zero-based local frame index.
• images/image_XX/000123.png and pcds/000123.pcd refer to the same sample.
• Line i in valid_frame.txt, params/lidars.txt, and params/cam*.txt corresponds to local index i.
• If present, line i in params/timestamps.txt is the LiDAR timestamp for local index i.
• PCD files contain x y z intensity fields and are stored in binary format.

Parameter Files

• cam0.txt ... cam*.txt: per-frame camera poses. Each line is a flattened row-major 4 x 4 matrix (1 x 16).
• cam0_to_lidar.txt ... cam*_to_lidar.txt: per-camera transforms. Each file contains one 1 x 17 row: [camera_id, flattened row-major 4 x 4 matrix].
• cams_to_lidar_gt.txt: ground-truth camera-to-LiDAR transforms, one 1 x 17 row per camera.
• cams_to_lidar_init.txt: initialization transforms, one 1 x 17 row per camera.
• intrinsics.txt: camera intrinsics as 3 x 3 matrices grouped by camera.
• lidars.txt: per-frame LiDAR poses, one flattened row-major 4 x 4 matrix per line.
• timestamps.txt: optional LiDAR timestamps, one scalar per local frame.

Scene selections and frame ranges are listed in CONFIG.md and metadata/scenes.csv.

License and Upstream Terms

The repository metadata uses license: other because the repository combines assets derived from multiple sources with different or separately specified terms. Read both of the following before use:

• LICENSE
• THIRD_PARTY_NOTICES.md

The repository-level notice does not replace or override any upstream dataset license, registration requirement, attribution requirement, or use restriction.

Citation

If you use this dataset or its format, cite TLC-Calib:

@article{jung2026targetless,
  title   = {{Targetless LiDAR-Camera Calibration with Neural Gaussian Splatting}},
  author  = {Jung, Haebeom and Kim, Namtae and Kim, Jungwoo and Park, Jaesik},
  journal = {IEEE Robotics and Automation Letters},
  volume  = {11},
  number  = {4},
  pages   = {4777--4784},
  year    = {2026},
  doi     = {10.1109/LRA.2026.3665066}
}

Also cite every upstream dataset whose scenes you use. Required upstream references are listed in THIRD_PARTY_NOTICES.md.