本项目下数据集包含数据
data_root
└─3D_DATA
└─3D_lidars
train_lidar.tar
val_lidar.tar
test_lidar.tar
raw_lidar_p0.tar.parta*
raw_lidar_p1.tar.parta*
raw_lidar_p2.tar.parta*
raw_lidar_p3.tar.parta*
raw_lidar_p4.tar.parta*
raw_lidar_p5.tar.parta*
raw_lidar_p6.tar.parta*
raw_lidar_p7.tar.parta*
raw_lidar_p8.tar.parta*
raw_lidar_p9.tar.parta*
Data orgainization
We organize the dataset by sensors and split unlabeled data into three different scales. Users can conviently choose which part of data to download according to different training intensions. Tar files larger than 20G are split into multiple files by linux split command with parta* extensions.
data_root
└─3D_DATA
├─3D_infos
│ train_infos.tar
│ val_infos.tar
│ test_infos.tar
│ raw_small_infos.tar
│ raw_medium_infos.tar
│ raw_large_infos.tar
├─3D_images
│ train_cam0[1356789].tar
│ val_cam0[1356789].tar
│ test_cam0[1356789].tar
│ raw_cam01_p[0-9].tar
│ raw_cam03_p[0-9].tar
│ raw_cam05_p[0-9].tar
│ raw_cam06_p[0-9].tar
│ raw_cam07_p[0-9].tar
│ raw_cam08_p[0-9].tar
│ raw_cam09_p[0-9].tar
└─3D_lidars
train_lidar.tar
val_lidar.tar
test_lidar.tar
raw_lidar_p0.tar.parta*
raw_lidar_p1.tar.parta*
raw_lidar_p2.tar.parta*
raw_lidar_p3.tar.parta*
raw_lidar_p4.tar.parta*
raw_lidar_p5.tar.parta*
raw_lidar_p6.tar.parta*
raw_lidar_p7.tar.parta*
raw_lidar_p8.tar.parta*
raw_lidar_p9.tar.parta*
After downloading desired part of data, user should put all downloaded files into the SAME folder discarding the formor folder structure. For example, both train_infos.tar, train_lidar.tar and train_cam0[1356789].tar should put in the same folder. After concatenation and extraction, file structure under data root directory is something like this
data_root
└─data
├─000000
│ ├─ cam01
│ │ ├─ frame_timestamp_1.jpg
│ │ ├─ frame_timestamp_2.jpg
│ │ ├─ ....
│ │ └─ frame_timestamp_n.jpg
│ ├─ cam03
│ │ ├─ ...
│ ├─ cam05
│ │ ├─ ...
│ ├─ cam06
│ │ ├─ ...
│ ├─ cam07
│ │ ├─ ...
│ ├─ cam08
│ │ ├─ ...
│ ├─ cam09
│ │ ├─ ...
│ ├─ lidar_roof
│ │ ├─ frame_timestamp_1.bin
│ │ ├─ frame_timestamp_2.bin
│ │ ├─ ....
│ │ └─ frame_timestamp_n.bin
│ └─ 000000.json
├─000001
├─000002
└─...
where n corresponds to the number of scenes in the specific road.