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---
license: mit
pretty_name: SynLiDAR
tags:
- lidar
- synthetic
- point-cloud
- autonomous-driving
- semantic-segmentation
- 3D-segmentation
---
# SynLiDAR Dataset
## Overview
**SynLiDAR** is a synthetic LiDAR dataset designed for autonomous driving research.
It contains high-quality simulated point cloud sequences and corresponding semantic annotations.
The dataset provides two variants:
- **FullDataset** — complete version for large-scale experiments (branch: `full`)
- **SubDataset** — smaller version suitable for prototyping, debugging, and benchmarking (branch: `sub`)
This HuggingFace repository uses **branches** to separate large data files and metadata:
- `main` → metadata, scripts, annotations
- `full` → FullDataset
- `sub` → SubDataset
---
## Dataset Structure
```
SynLiDAR/
├── FullDataset/
│ ├── sequences/
│ │ ├── 00.zip
│ │ ├── …
│ │ └── 12.zip
│ └── readme.txt
│
├── SubDataset/
│ ├── sequences/
│ │ ├── 00.zip
│ │ ├── …
│ │ └── 12.zip
│ └── readme.txt
│
├── annotations.yaml
├── read_data.py
└── README.md
```
> ⚠️ Some large sequences have been split into chunks (e.g. `06_part01.zip`, `06_part02.zip`, …) to avoid exceeding file size limits.
> These parts are functionally equivalent to the original `06.zip`.
---
## Contents
- `sequences/*.zip` — Each zip contains LiDAR frames from a single drive sequence
- `annotations.yaml` — Semantic categories and label mappings
- `read_data.py` — Example Python loader to read `.bin` point cloud files
- `readme.txt` — Original dataset notes
---
# 🔽 How to Download the Dataset
## Install huggingface_hub
```bash
pip install huggingface_hub
```
## Download Full Dataset (branch: full)
```python
from huggingface_hub import snapshot_download
path = snapshot_download(
repo_id="AR-X/SynLiDAR",
repo_type="dataset",
revision="full",
local_dir="./SynLiDAR", # specify your desired path
)
print(path)
```
## Download Sub Dataset (branch: sub)
```python
from huggingface_hub import snapshot_download
path = snapshot_download(
repo_id="AR-X/SynLiDAR",
repo_type="dataset",
revision="sub",
local_dir="./SynLiDAR", # specify your desired path
)
print(path)
```
## 🔧 Merging Split ZIP Files
Some sequences in the full branch were too large (>50GB) and are stored as multiple parts:
Example:
```
06_part01.zip
06_part02.zip
...
06_part11.zip
```
These parts correspond to a single original archive. You can merge them into a single folder using Python:
```python
from pathlib import Path
sequence_dir = Path("SynLiDAR/FullDataset/sequences")
# find bases that have part files
bases = sorted({p.stem.split("_part")[0] for p in sequence_dir.glob("*_part*.zip")})
for base in bases:
parts = sorted(sequence_dir.glob(f"{base}_part*.zip"))
output_zip = sequence_dir / f"{base}.zip"
print(f"[{base}] merging {len(parts)} parts -> {output_zip}")
with open(output_zip, "wb") as out:
for p in parts:
with open(p, "rb") as f:
out.write(f.read())
print("All sequences merged.")
```
## Citation
```
@inproceedings{xiao2022transfer,
title={Transfer learning from synthetic to real lidar point cloud for semantic segmentation},
author={Xiao, Aoran and Huang, Jiaxing and Guan, Dayan and Zhan, Fangneng and Lu, Shijian},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={36},
number={3},
pages={2795--2803},
year={2022}
}
```
⚠️ **Disclaimer**: This dataset is intended for research and educational usage.
Make sure to respect local regulations when training or deploying autonomous driving systems. |