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https://github.com/LiDAR-Perception/LiDAR-CS
LiDAR-CS Dataset
https://github.com/LiDAR-Perception/LiDAR-CS
Last synced: 3 months ago
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LiDAR-CS Dataset
- Host: GitHub
- URL: https://github.com/LiDAR-Perception/LiDAR-CS
- Owner: LiDAR-Perception
- License: gpl-3.0
- Created: 2021-11-18T02:45:57.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-02-11T04:21:07.000Z (over 1 year ago)
- Last Synced: 2024-01-21T04:56:29.271Z (5 months ago)
- Language: Shell
- Size: 5.46 MB
- Stars: 35
- Watchers: 3
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-mobile-robotics - LIDAR-CS - Sensors** (Datasets)
README
# LiDAR-CS
**LiDAR** Dataset with **C**ross-**S**ensors (LiDAR-CS Dataset), which contains large-scale annotated LiDAR point cloud under 6 groups of different sensors but with the same correspondence scenarios, captured from hybrid realistic LiDAR simulator. As far as we know, LiDAR-CS Dataset is the first dataset focused on the sensor (e.g., the points distribution) domain gaps for 3D object detection in real traffic.
**Update**:
We also provide point cloud data with different sensor heights (1.0m, 1.5m, 2.0m, 2.5m, 3.0m).
## Download
This is the official GitHub repository for LiDAR-CS dataset.
1. The download links can be found in `download_data.sh`
2. We also support Baidu Yunpan. Link：[https://pan.baidu.com/s/1NyPziUeqfBSv6rgZBUspkQ](https://pan.baidu.com/s/1NyPziUeqfBSv6rgZBUspkQ) with extraction code: **x6o2**
## Data Sample
## Getting Started
### 1. Data Prepare
Uncompress all the compressed files, for example
```
# For the normal compressed file
tar zxf VLD-16.tar.gz
# For the compressed file which is split due to the size limitation
cat VLD-128.tar.gz* | tar -zxf -
```
All the file will be organized as,
```
├── Livox
│ ├── bin
│ └── label
├── ONCE-40
│ ├── bin
│ └── label
├── VLD-128
│ ├── bin
│ └── label
├── VLD-16
│ ├── bin
│ └── label
├── VLD-32
│ ├── bin
│ └── label
└── VLD-64
│ ├── bin
│ └── label
└── splits
├── test.txt
└── train.txt
```
### 2. Load Data
We follow KITTI to store the point cloud into binary files and the annotation results are stored in text files that are easy to parse.
Here is a python sample code to load the point cloud and the annotation file.
```python
import numpy as np
def get_label(label_file):
labels = np.loadtxt(label_file, dtype=str)
if len(labels.shape) == 1:
labels = labels[None, :]
types, labels = labels[:, 0], labels[:, 1:].astype(np.float32)
return types, labels
pc_path = 'VLD-16/bin/000000.bin'
label_path = 'VLD-16/label/000000.txt'
# the (x, y, z, intensity) are stored in binary
xyzi = np.fromfile(pc_path, dtype=np.float32).reshape(-1, 4)
# types store the class names for the objects
# labels store a n * 7 ndarry and 7 is for (x, y, z, lenght, width, height, angle) in LiDAR coordinate.
types, labels = get_label(label_path)
```
## Todo List
- [ ] More sensors will be supported.
- [ ] Update the evaluation code.
- [ ] Update the cross evaluation code.
## Changelog
+ **v1.0**: support 6 sensors, including VLD-16/32/64/128, Livox and ONCE-40
+ **v1.1**: support different sensor heights (1.0m, 1.5m, 2.0m, 2.5m, 3.0m) for VLD-64
## Citation
If you find this dataset useful in your research, please consider cite:
```
@article{fang2023lidar,
title={LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object Detection},
author={Fang, Jin and Zhou, Dingfu and Zhao, Jingjing and Tang, Chulin and Xu, Cheng-Zhong and Zhang, Liangjun},
journal={arXiv preprint arXiv:2301.12515},
year={2023}
}
```