This endpoint creates a lidarsegmentation
task. In this task, one of our Taskers views outputs from a series of LIDAR frames, along with optional camera data, and annotates where different objects exist in the 3D space by assigning a class to each LidarPoint
.
This type of task can be created on its own, or you can create a task based on an already completed Lidar Annotation task.
The required parameters for this task are labels
, attachments
, and attachment_type
.
- The
callback_url
is the URL which will be POSTed on task completion, and is described in more detail in the callbacks section. - The
labels
array lists the object classes for which semantic information is desired.- Instance labels are supported, by specifying
instance_label: true
when defining the label. For example,['Road', {'choice': 'Pedestrian', 'instance_label': true}]
. - Nested labels are also supported for these labels, and may be specified in the same format as noted in our documentation. For example,
['Vehicle', {'choice': 'Pedestrian', 'subchoices': ['Adult', 'Child']}]
.
- Instance labels are supported, by specifying
- The
attachments
will be a list of links to external JSON files, each following the definition of aFrame
as specified here.
You should provide additional markdown-enabled instructions via the instruction parameter.
It is strongly recommended for you to flesh out your Markdown instructions with many examples of tasks being done correctly and incorrectly.
If successful, Scale will immediately return the generated task object, of which you should at least store the task_id