[Masterclass Recap] Building Robust 3D Data Pipelines: From Manual Cuboids to Scalable Workflows

Manual 3D cuboid labeling struggles to keep pace with the increasing complexity and size of point cloud datasets, leading to inefficiencies in building production-ready autonomous systems. Encord's masterclass introduces a shift from manual-first labeling to human-in-the-loop automation workflows, where models generate initial 3D bounding boxes, allowing humans to verify and refine them. This approach significantly accelerates the annotation process, as annotators transition from constructing labels frame by frame to reviewing and handling edge cases, which results in a five to tenfold increase in speed without compromising quality. The integration of automation into the annotation workflow creates a scalable system that maintains consistency by leveraging temporal context and reduces cognitive load through visualization modes. As corrected annotations feed back into the model, the system's accuracy and speed improve over time, making it possible to aggressively scale datasets for autonomous driving and robotics systems. The future of 3D data pipelines lies in moving beyond traditional manual cuboid workflows to embrace this automated, human-in-the-loop approach, transforming annotation from a bottleneck into a robust and scalable engine for AI system development.