Start From Completed LIDAR Task

curl "https://api.scale.com/v1/task/lidarsegmentation" \
  -u "{{ApiKey}}:" \
  -H "Content-Type: application/json" \
  -X POST
  -d '
{
  "callback_url": "http://www.example.com/callback",
  "instruction": "Color all points making up the desired objects in the scene.",
  "labels": [
    "car", "pedestrian", "vegetation", "road"
  ],
  "attachment_type": "json",
  "lidar_task": "5cc1bfaa34489c006cfd6fc3",
  "lidar_task_frames": [1,2],
}'

Alternatively, you can start from a completed LiDAR Annotation task and use it as the source for the Semantic Segmentation work. This will persist the cuboids that you got from the annotation step. Furthermore, you can specify which subset of frames should be included as the source material. The following differences from the previous approach will take effect:

  • A new parameter name lidar_task will contain the identifier of the LiDAR Annotation task to be used as source. The lidar_task needs to be in completed state.
  • You don't need to add the attachments and attachment_type parameters as the Frame objects will be taken from the source LiDAR Annotation task.
  • A new optional parameter appears, named lidar_task_frames, allows you to specify an array of frame indexes to select which subset of frames you want to use from the LiDAR Annotation task. If omitted all frames will be used.
    • For example, assuming we start from a completed LiDAR Annotation task with five frames and we wanted to use all frames except the last one, the parameter will look like lidar_task_frames: [0, 1, 2, 3].
  • The labels parameter needs to be a super set of the set used on the original LiDAR Annotation task.