Introducing 3D Point Cloud Labeling Workflows using Amazon SageMaker Ground Truth
Amazon SageMaker Ground Truth now supports 3D Point Cloud Labeling Workflows so it’s easy to build highly accurate training datasets for three dimensional (3D) data.
Three dimensional (3D) point clouds are most commonly captured using Light Detection and Ranging (LIDAR) devices in order to generate a 3D understanding of a physical space at a single point in time. Now using SageMaker Ground Truth, you can use several data labeling techniques including objection tracking, and semantic segmentation techniques for your 3D point cloud data. 3D point clouds are generated by autonomous vehicles so you can now use SageMaker Ground Truth for the most common data label tasks required to train autonomous vehicles.
To learn more about the new 3D Point Cloud labeling workflows, read the blog post and refer to the documentation.