Create a 3D point cloud semantic segmentation labeling job - Amazon SageMaker
Create a labeling job (console)Create a labeling job (API)

Create a 3D point cloud semantic segmentation labeling job

You can create a 3D point cloud labeling job using the SageMaker console or API operation, CreateLabelingJob. To create a labeling job for this task type you need the following:

Additionally, make sure that you have reviewed and satisfied the Assign IAM Permissions to Use Ground Truth.

Use one of the following sections to learn how to create a labeling job using the console or an API.

Create a labeling job (console)

You can follow the instructions Create a Labeling Job (Console) in order to learn how to create a 3D point cloud semantic segmentation labeling job in the SageMaker console. While you are creating your labeling job, be aware of the following:

  • Your input manifest file must be a single-frame manifest file. For more information, see Create a Point Cloud Frame Input Manifest File.

  • Automated data labeling and annotation consolidation are not supported for 3D point cloud labeling tasks.

  • 3D point cloud semantic segmentation labeling jobs can take multiple hours to complete. You can specify a longer time limit for these labeling jobs when you select your work team (up to 7 days, or 604800 seconds).

Create a labeling job (API)

This section covers details you need to know when you create a labeling job using the SageMaker API operation CreateLabelingJob. This API defines this operation for all AWS SDKs. To see a list of language-specific SDKs supported for this operation, review the See Also section of CreateLabelingJob.

The page, Create a Labeling Job (API), provides an overview of the CreateLabelingJob operation. Follow these instructions and do the following while you configure your request:

  • You must enter an ARN for HumanTaskUiArn. Use arn:aws:sagemaker:<region>:394669845002:human-task-ui/PointCloudSemanticSegmentation. Replace <region> with the AWS Region you are creating the labeling job in.

    There should not be an entry for the UiTemplateS3Uri parameter.

  • Your LabelAttributeName must end in -ref. For example, ss-labels-ref.

  • Your input manifest file must be a single-frame manifest file. For more information, see Create a Point Cloud Frame Input Manifest File.

  • You specify your labels and worker instructions in a label category configuration file. See Labeling category configuration file with label category and frame attributes reference to learn how to create this file.

  • You need to provide a pre-defined ARNs for the pre-annotation and post-annotation (ACS) Lambda functions. These ARNs are specific to the AWS Region you use to create your labeling job.

    • To find the pre-annotation Lambda ARN, refer to PreHumanTaskLambdaArn. Use the Region you are creating your labeling job in to find the correct ARN. For example, if you are creating your labeling job in us-east-1, the ARN will be arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudSemanticSegmentation.

    • To find the post-annotation Lambda ARN, refer to AnnotationConsolidationLambdaArn. Use the Region you are creating your labeling job in to find the correct ARN. For example, if you are creating your labeling job in us-east-1, the ARN will be arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentation.

  • The number of workers specified in NumberOfHumanWorkersPerDataObject should be 1.

  • Automated data labeling is not supported for 3D point cloud labeling jobs. You should not specify values for parameters in LabelingJobAlgorithmsConfig.

  • 3D point cloud semantic segmentation labeling jobs can take multiple hours to complete. You can specify a longer time limit for these labeling jobs in TaskTimeLimitInSeconds (up to 7 days, or 604800 seconds).