- [2023/12]: Our paper 3DOPFormer: 3D Occupancy Perception from Multi-Camera Images with Directional and Distance Enhancement accepted by IEEE Transactions on Intelligent Vehicles
Physical drawings and schematics of the ground robot is given below.
Figure 1. The WHEELTEC ground robot equipped with a lidar and four RGB cameras. The directions of the sensors are marked in different colors, red for X, green for Y and blue for Z. The camera id are named 0~3 from front_right to front_left successively.
All the sensors and their most important parameters are listed as below:
-
LIDAR LeiShen Intelligent C16-151B, 360 Horizontal Field of View (FOV), -15 to +15 Vertical FOV, 20Hz, Max Range 200 m, Range Resolution 3 cm, Horizontal Angular Resolution 0.36°.
-
RGB Camera HIKVISION U64, 1280*720, 81 H-FOV, 90 D-FOV, 30 Hz
We make public ALL THE SEQUENCES with their imgs captured from 4 cameras and lidar_txt/lidar_pcd files now.
All the sequences are acquired in Shandong University, Qingdao, including indoor and outdoor scenes. Dataset link is: ML3DOP
Figure 2. An image frame acquired by four cameras.
Figure 3. A lidar frame acquired by lidar.
An overview of ML3DOP is given in the tables below:
Indoor
| Scenario | Number | Size of imgs/GB | Size of lidar_txt/GB | Size of lidar_pcd/GB | Duration/s |
|---|---|---|---|---|---|
| Canteen | 3 | 17.4 | 13.7 | 27.3 | 747 |
| Fengyu | 1 | 8.3 | 5.1 | 9.5 | 261 |
| Huiwen | 2 | 3.3 | 5.7 | 10.6 | 272 |
| Library | 1 | 5.1 | 4.5 | 11.9 | 254 |
| Museum | 2 | 10.2 | 13.4 | 32 | 669 |
| N5 | 4 | 18.2 | 26.6 | 51.8 | 1295 |
| N7 | 2 | 3.7 | 5.9 | 11.2 | 271 |
| Shoppingmall | 2 | 5.2 | 6.2 | 11.7 | 312 |
| Zhensheng | 1 | 7.3 | 11.6 | 21.7 | 580 |
| TOTAL | 18 | 78.7 | 92.7 | 187.7 | 4661 |
Outdoor
| Scenario | Number | Size of imgs/GB | Size of lidar_txt/GB | Size of lidar_pcd/GB | Duration/s |
|---|---|---|---|---|---|
| Between_zhensheng_and_huagang | 1 | 6.0 | 4.7 | 8.8 | 186 |
| Dark | 1 | 3.2 | 3.2 | 6.0 | 155 |
| Dark_library | 1 | 15.1 | 12.5 | 23.7 | 739 |
| Fengyu_north | 1 | 16.4 | 6.8 | 13.0 | 423 |
| Huagang-zhensheng_west | 1 | 28.8 | 13.7 | 25.8 | 794 |
| Museum | 1 | 20.0 | 8.4 | 17.7 | 788 |
| N1_west-N5_north | 1 | 28.9 | 14.8 | 27.8 | 837 |
| N5_north-N1_west | 1 | 26.3 | 12.2 | 22.7 | 661 |
| Playground_south | 1 | 3.6 | 2.6 | 4.9 | 143 |
| Zhensheng_north | 1 | 20.2 | 10.1 | 19.2 | 595 |
| Zhensheng_north-N1_north | 1 | 5.5 | 2.4 | 4.7 | 155 |
| TOTAL | 11 | 174.0 | 91.4 | 174.3 | 5476 |
Our model 3DOPFormer has currently only been trained and tested on indoor scene dataset, the division of the indoor scene dataset is as follows:
| train | val | test |
|---|---|---|
| canteen_floor_1 | huiwen_floor_1 | N7_floor_1 |
| canteen_floor_2 | N5_floor_1_north | N5_floor_1_south |
| canteen_floor_3 | ||
| fengyu | ||
| huiwen_floor_2 | ||
| library_floor_2 | ||
| museum_floor_2 | ||
| museum_floor_4 | ||
| N5_floor_1 | ||
| N5_floor_2 | ||
| N7_floor_2 | ||
| shoppingmall_floor_1 | ||
| shoppingmall_floor_2 | ||
| zhensheng |
| Sequence name | Collection date | Size of imgs/GB | Size of lidar_txt/GB | Size of lidar_pcd/GB | Duration/s |
|---|---|---|---|---|---|
| indoor_canteen_floor_1 | 2023-03-07 | 6.1 | 4.8 | 10.6 | 254 |
| indoor_canteen_floor_2 | 2023-03-07 | 5.8 | 4.0 | 7.5 | 219 |
| indoor_canteen_floor_3 | 2023-03-07 | 5.5 | 4.9 | 9.2 | 274 |
| indoor_fengyu | 2023-03-09 | 8.3 | 5.1 | 9.5 | 261 |
| indoor_huiwen_floor_1 | 2023-03-08 | 1.5 | 2.8 | 5.2 | 129 |
| indoor_huiwen_floor_2 | 2023-03-08 | 1.8 | 2.9 | 5.4 | 143 |
| indoor_library_floor_2 | 2023-03-06 | 5.1 | 4.5 | 11.9 | 254 |
| indoor_museum_floor_2 | 2023-03-08 | 4.1 | 5.4 | 13.7 | 266 |
| indoor_museum_floor_4 | 2023-03-08 | 6.1 | 8.0 | 18.3 | 403 |
| indoor_N5_floor_1 | 2023-03-06 | 7.4 | 12.1 | 23.2 | 596 |
| indoor_N5_floor_2 | 2023-03-06 | 7.7 | 11.6 | 22.3 | 577 |
| indoor_N5_floor_1_north | 2022-12-04 | 1.6 | 1.6 | 2.9 | 59 |
| indoor_N5_floor_1_south | 2022-12-04 | 1.5 | 1.3 | 3.4 | 63 |
| indoor_N7_floor_1 | 2023-03-07 | 1.5 | 2.6 | 5.0 | 112 |
| indoor_N7_floor_2 | 2023-03-07 | 2.2 | 3.3 | 6.2 | 159 |
| indoor_shoppingmall_floor_1 | 2023-03-07 | 2.7 | 2.7 | 5.2 | 137 |
| indoor_shoppingmall_floor_2 | 2023-03-07 | 2.5 | 3.5 | 6.5 | 175 |
| indoor_zhensheng | 2023-03-06 | 7.3 | 11.6 | 21.7 | 580 |
| Sequence name | Collection date | Size of imgs/GB | Size of lidar_txt/GB | Size of lidar_pcd/GB | Duration/s |
|---|---|---|---|---|---|
| outdoor_between_zhensheng_and_huagang | 2023-03-07 | 6.0 | 4.7 | 8.8 | 186 |
| outdoor_dark | 2023-03-07 | 3.2 | 3.2 | 6.0 | 155 |
| outdoor_dark_library | 2023-03-08 | 15.1 | 12.5 | 23.7 | 739 |
| outdoor_fengyu_north | 2023-03-09 | 16.4 | 6.8 | 13.0 | 423 |
| outdoor_huagang-zhensheng_west | 2023-03-09 | 28.8 | 13.7 | 25.8 | 794 |
| outdoor_museum | 2023-03-07 | 20.0 | 8.4 | 17.7 | 788 |
| outdoor_N1_west-N5_north | 2023-03-06 | 28.9 | 14.8 | 27.8 | 837 |
| outdoor_N5_north-N1_west | 2023-03-06 | 26.3 | 12.2 | 22.7 | 661 |
| outdoor_playground_south | 2023-03-07 | 3.6 | 2.6 | 4.9 | 143 |
| outdoor_zhensheng_north | 2023-03-06 | 20.2 | 10.1 | 19.2 | 595 |
| outdoor_zhensheng_north-N1_north | 2023-03-07 | 5.5 | 2.4 | 4.7 | 155 |
- python=3.9.5
- numpy=1.20.2
- pip=21.1.1
- tqdm=4.61.2
- pip:
- pyyaml==6.0
- dpkt==1.9.7.2
As we use the LSC16-[Client] software provided by LeiShen Intelligent Company to acquire lidar data on Windows platform in the form of pcap file, so that we need to extract lidar data frame from these pcap files.
Firstly, we filter out data packages using Wireshark software due to the presence of both data and device packages in the pcap file. The filtered pcap files are provided in our dataset: lidar_pcap_indoor.zip and lidar_pcap_outdoor.zip.
Then, git clone this project, check the parameters in params.yaml, if on Linux platform, run:
bash run_indoor.sh
or
bash run_outdoor.sh
You can change the --path and --out-dir parameter in above .sh files according to the actual situation.
If on Windows platform, run:
python main.py --path your_path --out-dir your_out-dir --config=.\params.yaml
After this operation, we get TXT files/PCD files named as index and time (Beijing).
Full 360° frame store in a file.
All TXT files have the following fields:
Timestamp, Laser_ID, X [m], Y [m], Z [m], Intensity [0-255], Vertical_angle [Angle system], Horizontal_angle [Angle system], Distance [m]
All PCD files have the following fields:
X [m], Y [m], Z [m], Intensity [0-255]
The TXT files and PCD files are provided in our dataset: lidar_indoor_txt.zip, lidar_outdoor_txt.zip, lidar_indoor_pcd.zip, lidar_outdoor_pcd.zip.
This part is based on https://github.com/hitxing/Lidar-data-decode/ which supports LSC32. Actually, this project can support any lidar as long as you change the parameters follow the corresponding technical manual.
For the convenience of using this dataset, we has generated a .pkl file, which stores a data_dict. In this data_dict, imgs_path are classified by img0_key and camera_id, lidar_path are classified by img0_key (img0_key is the imgs_path of camera 0).
If on Linux platform, run:
bash data_pkl.sh
You can change the --imgs_path and --lidar_path parameter in above .sh files according to the actual situation.
If on Windows platform, run:
python data_pkl.py --imgs_path your_imgs_path --lidar_path your_lidar_path
After this operation, we get data_all.pkl. This file is provided in our dataset: data_pkl.zip.
Before running, you need to make sure the machine's time zone is Beijing time zone to get the correct timestamp.
Place the calibration board in front of the camera, we record calibration videos for each camera, then use the Autoware calibration toolbox in the ROS environment to calibrate four cameras separately. Calibration files are provided in our dataset: calibration.zip.
This work is licensed under MIT license, which is provided for academic purpose as an international license.