README.md

---
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.