Awesome

Waymo_Kitti_Adapter

This is a tool converting Waymo open dataset format to Kitti dataset format.

Author: Yao Shao

Contact: yshao998@gmail.com

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 buffer folder and paste them into waymo open dataset folder.
Copy adapter.py to waymo-od folder. 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 folder kitti_dataset/. Feel free to change them to your preferred path by rewriting the configurations in adapter.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.