As autonomous systems continue to evolve, the diversity and complexity of required annotations have grown exponentially. Challenges are ranging from multi-sensor fusion data and complex scene understanding to early fusion approaches and voxel representation support. Modern annotation processes demand the right expertise, tooling, workflows and workforce to deliver high-quality datasets at scale. In this session, Segments.ai delves into the key types of annotations currently seen in large-scale automotive and robotics projects and how an integrated approach can optimize accuracy, speed, and scalability in data labeling. We will …
- Explore the range of annotation types, including LiDAR, radar, and multi-sensor data labeling.
- Understand how to implement hybrid workflows combining manual, ML-assisted, and automated tooling for greater efficiency.
- Gain insights into how Segments.ai’s platform allows for the next generation of data labeling in autonomous systems.
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