Manual 3D cuboid labeling doesn’t scale.

As point cloud datasets grow larger and more complex, drawing cuboids frame by frame becomes the slowest, and most expensive, part of building production-ready models.

In this 20-minute masterclass, we’ll show how teams are replacing manual-first 3D annotation with autolabeling workflows, where models generate high-quality cuboids and humans focus on review, refinement, and edge cases.

In this fast-paced session you’ll learn how to:

• Use 3D cuboid autolabeling to generate high-quality LiDAR annotations automatically
• Review, refine, and approve model-generated cuboids instead of drawing them from scratch
• Combine temporal context and point cloud data to improve cuboid consistency
• Decide where human-in-the-loop review adds the most value

Who this is for:

ML engineers building autonomous driving or robotics systems. Annotation and data operations leaders scaling 3D labeling efforts. Teams looking to move beyond manual cuboids to automated 3D pipelines

Register to participate in the session live or to get a recording.