Synthetic automotive LiDAR dataset with radial velocity additional feature - (x,y,z,v )

The synthetic dataset was generated using KITTI-like specifications and annotations format. It is comprised by the KITTI standard  folders: label_2, image_2 and calib. Furthermore, there is a velodyne file for each of the following use cases:

Point cloud 1: (x,y,z, (Bool)Is_Object): In this point cloud, the best performance of the Deep Learning model is expected as ground truth information is provided as the additional feature of each point.

Point cloud 1A: (x,y,z, (Bool)Is_Car): the additional feature of each point that belongs to an object of the ’Car’ type has a Boolean 1.0 value; contrariwise, the 0.0 value was used. File: velodyne_1A_isCar;

Point cloud 1B: (x,y,z, (Bool)Is_Ped): the additional feature of each point that belongs to an object of the ’Pedestrian’ type has a Boolean 1.0 value; contrariwise, the value 0.0 was used.  File: velodyne_1A_isPed.

Point cloud 2: (x,y,z, (Float)Radial_Velocity): this point cloud has the relative radial velocity as an additional feature for each point. File: velodyne_2_radial_velocity;

Point cloud 3: (x,y,z,(Float)Car_Absolute_Speed): in this point cloud, every point of a ’Car’ type object
has the absolute speed of the object as the additional feature. File: velodyne_3_car_abs_speed;

Point cloud 4: (x,y,z,(Bool)Car_Is_Moving): the additional feature of a ’Car’ type object is a Boolean value that is set to 1.0 if the vehicle is moving, contrariwise is set to 0.0 for other object categories or if the vehicle is static. File: velodyne_4_car_is_moving;

Point cloud 5: (x,y,z,0): no additional feature information. If desired, requires post-processing to convert to (x,y,z) or changing the toolbox point cloud configuration to not consider the additional feature. File: velodyne_5_xyz;

Additionally, the label split for testing and training sets used can be found at file: Labels_split.

This work was made as part of a master thesis. For further details, please check the dataset generation source code [1]. Any further questions please contact Leandro Alexandrino ([e-mail]).

[1] - Fork deepgtav-presil - leandro alexandrino, https://github.com/leandroalexandrino1995/DeepGTAVPreSIL.