3D point cloud labeling tool
for robotics and autonomous vehicles

Annotate lidar and RGBD data with advanced labeling interfaces.
Label in-house or with an external workforce.

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Full-featured labeling tool

Cuboid (3D bounding box) labels
Point cloud segmentation
Display point clouds with color or intensity values
Sensor fusion: label on aligned camera images

Label sequences with interpolation

Label sequences of point clouds, and optionally define the ego pose for each frame. Use keyframe interpolation to speed up bounding box labeling.

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Bounding box keyframe interpolation

Merged point cloud view

Label static objects directly in a merged point cloud view. Thanks to our 3D tiles technology, there is no limit on the size of the merged point clouds.

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Label dynamic objects with batch mode

To speed up labeling of dynamic objects in point cloud sequences, you can use batch mode. Batch mode displays an object in all frames in a simple interface. That way, you can easily see where adjustments are needed.

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Why ML teams choose Segments.ai

    from segments import SegmentsClient

    client = SegmentsClient("api_key")

    dataset = client.add_dataset(

    sample = client.add_sample(
TensorFlow, AWS, Hugging Face, and PyTorch logo

Easy to set up and integrate

Integrate data labeling into your existing ML pipelines and workflows using our simple yet powerful Python SDK.

Upload data, download labels, and manage datasets programmatically

Automatically trigger actions using webhooks

Connect your cloud provider (AWS, Google Cloud, Azure)

Export to popular ML frameworks (PyTorch, TensorFlow, Hugging Face 🤗)

Label in-house or outsource

Onboard your own workforce or use one of our workforce partners. Our management tools make it easy to label and review large datasets together.

Give collaborators access to specific datasets and choose their permissions

Onboard an external workforce from our labeling service partners

Set up a reviewing step and communicate with labelers via issues

Track labeling performance through metrics and custom dashboards.

Model predictions vs. corrected labels
Model predictions vs. corrected labels

Set up advanced workflows

Implement active learning by using your model predictions to find failure cases and to speed up labeling.


Label images and point clouds on Segments.ai


Train a model and keep it on your side. Only upload model predictions


Compare predictions with ground-truth labels and use our search syntax to find failure cases


Use model predictions as prelabels to speed up labeling

Start building better datasets today

Try Segments.ai free for 14 days. No credit card required.

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