Waymo_Kitti_Adapter
This is a tool converting Waymo open dataset format to Kitti dataset format.
Author: Yao Shao
Contact: [email protected]
Instruction
- Follow the instructons in QuickStart.md, clone the waymo open dataset repo, build and test it.
- Clone this repo to your computer, then copy the files in
protocol bufferfolder and paste them intowaymo open datasetfolder. - Copy adapter.py to
waymo-odfolder. Open adapter.py and change the configurations at the top so that it suits to your own computer's path. - The folder tree may look like this, the downloaded waymo dataset should be in the folder named
waymo_dataset, and the generated kitti dataset should be in the folderkitti_dataset/. Feel free to change them to your preferred path by rewriting the configurations inadapter.py.
.
├── adapter.py
├── waymo_open_dataset
│ ├── label_pb2.py
│ ├── label.proto
│ └── ...
├── waymo_dataset
│ └── frames
├── kitti_dataset
│ ├── calib
│ ├── image_0
│ ├── image_1
│ ├── image_2
│ ├── image_3
│ ├── image_4
│ ├── lidar
│ └── label
├── configure.sh
├── CONTRIBUTING.md
├── docs
├── LICENSE
├── QuickStart.md
├── README.md
├── tf
├── third_party
├── tutorial
└── WORKSPACE
- Run adapter.py.
Data specification
Cameras
Waymo dataset contains five cameras:
FRONT = 0;
FRONT_LEFT = 1;
FRONT_RIGHT = 2;
SIDE_LEFT = 3;
SIDE_RIGHT = 4;
all the names below with post-fix 0-4 is corresponding to these five cameras.
Label
label_0 to label_4 contains label data for each camera and label_all fonder contain all the labels.
All in vehicle frame.
For each frame, here is the data specification:
#Values Name Description
----------------------------------------------------------------------------
1 type Describes the type of object: 'Car', 'Van', 'Truck',
'Pedestrian', 'Person_sitting', 'Cyclist', 'Tram',
'Misc' or 'DontCare'
1 truncated Float from 0 (non-truncated) to 1 (truncated), where
truncated refers to the object leaving image boundaries
1 occluded Integer (0,1,2,3) indicating occlusion state:
0 = fully visible, 1 = partly occluded
2 = largely occluded, 3 = unknown
1 alpha Observation angle of object, ranging [-pi..pi]
4 bbox 2D bounding box of object in the image (0-based index):
contains left, top, right, bottom pixel coordinates
3 dimensions 3D object dimensions: height, width, length (in meters)
3 location 3D object location x,y,z in camera coordinates (in meters)
1 rotation_y Rotation ry around Y-axis in camera coordinates [-pi..pi]
1 camera_num the camera number which the object belongs to, only exist
in label_all
Calib
P0-P4 : intrinsic matrix for each camera
R0_rect : rectify matrix
Tr_velo_to_cam_0 - Tr_velo_to_cam_4 : transformation matrix from vehicle frame to camera frame
Image
image_0 - image_4 : images for each
Lidar
Point cloud in vehicle frame.
x y z intensity
For more details, see readme.txt by KITTI.