# 3D Point Cloud Task types
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You can use Ground Truth 3D point cloud labeling modality for a variety of use cases. The following list briefly describes each 3D point cloud task type. For additional details and instructions on how to create a labeling job using a specific task type, select the task type name to see its task type page. 
+ [3D point cloud object detection](https://docs.amazonaws.cn/sagemaker/latest/dg/sms-point-cloud-object-detection.html) – Use this task type when you want workers to locate and classify objects in a 3D point cloud by adding and fitting 3D cuboids around objects. 
+ [3D point cloud object tracking](https://docs.amazonaws.cn/sagemaker/latest/dg/sms-point-cloud-object-tracking.html) – Use this task type when you want workers to add and fit 3D cuboids around objects to track their movement across a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames.
+ [3D point cloud semantic segmentation](https://docs.amazonaws.cn/sagemaker/latest/dg/sms-point-cloud-semantic-segmentation.html) – Use this task type when you want workers to create a point-level semantic segmentation mask by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify. 
+  3D point cloud adjustment task types – Each of the task types above has an associated *adjustment* task type that you can use to audit and adjust annotations generated from a 3D point cloud labeling job. Refer to the task type page of the associated type to learn how to create an adjustment labeling job for that task.