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Session

Solution Study

Tuesday, September 30

09:30 AM - 10:00 AM

Live in Berlin

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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.
PE
Presentation

Speaker

Otto Debals

CEO & Co-Founder, Segments.ai

Company

Segments.ai

Spend less time on labeling and more time building, running, and optimizing your ML algorithms. Segments.ai’s multi-sensor labeling platform lets you combine your 3D point cloud data and 2D image data in the same task to get the clearest picture possible. Upload your 3D data, then fuse information from multiple sensors to make annotating and categorizing your point clouds easier. Project and copy labels from 3D sensors to 2D sensors with advanced automation. You’ll get a faster and more consistent labeling workflow. The result: You get consistent labels across modalities and time, and you will have better, more accurate data to feed into your machine-learning algorithms. Your team will spend less time on quality checks and corrections.

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