3D Point Cloud Labeling Tool

Annotate lidar and other point cloud data with intuitive labeling interfaces.
Easy to set up and integrate into your workflow.

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Flexible labeling interfaces

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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Model-assisted labeling

Label training data faster by leveraging your model. Upload model predictions for new data and correct the predictions in the labeling interface.

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Why ML teams choose Segments.ai


  from segments import SegmentsClient

  client = SegmentsClient("api_key")

  dataset = client.add_dataset(
    "dataset_name", 
    task_type, 
    task_attributes
  )

  sample = client.add_sample(
    "user/dataset_name", 
    "sample_name", 
    attributes
  )

Easy to set up and integrate

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

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

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

Manage your workforce

Collaborate with your team or onboard an external workforce. Our management tools make it easy to build 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.

Set up advanced workflows

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

Label images, point clouds, and text 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

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