Get consistent and accurate data with multi-sensor labeling

  • Get consistent annotations across time and modalities

  • Label multiple sensors at the same time

  • Spend less time on quality checks and corrections
multi-sensor data labeling platform benefits: overlay, projection and consisteny

Trusted by computer vision engineers across robotics and AV companies

Get 3D and image data labeled together’s labeling platform lets you label across multiple sensors at the same time — so you can improve the quality of all data across the board.

Streamline data labeling, cut down on reconciliation time, and optimize your model training with synced tracking IDs.

Project and copy labels from 3D sensors to 2D sensors with advanced automation. You’ll get a faster and more consistent labeling workflow.

Simultaneously view 2D image and 3D point cloud data to give labelers more context. You’ll get better quality data and cut out the noise. had all the features required for us to label our images and point clouds, such as overlaying 3D point clouds onto 2D images. This proved very useful at giving labelers context for what they were looking at in 3D, especially in noisy shots.

Sarina H.

Robotics Software Engineer
OCIUS Technology Limited

Label 3D point clouds efficiently

Annotate objects in 3D in just a few clicks. You can upload point clouds of unlimited size, then label them with smart interfaces and tools such as the merged 3D mode, the batch mode, and automated tracking.

The best part? You can use that 3D data to enrich your 2D labeling efforts. Instead of drawing hundreds of bounding boxes, all you need is a single cuboid in a 3D point cloud. Then, you can project that cuboid to all the images where the object appears and voilà — you’ve just saved your team precious time and effort.

Get from PoC to production, faster

Accurate labels. Faster workflows. helps machine learning teams and engineers label their data at scale (without worrying about quality).

Upload point clouds of unlimited size2023-11-06T10:47:45+01:00 can handle all the data you have. Our 3D Point Cloud interface splits point clouds into 3D tiles for faster loading times and a reduced GPU workload (similar to Google Map’s use of low to high-resolution images). And, the backend technology makes sure the UX stays fast and responsive.

Label dynamic objects with Batch Mode2023-11-06T10:53:15+01:00

Batch Mode lets you view all instances of an object in a sequence on a single, scrollable screen. Correcting labels is as easy as modifying the cuboid in one row, then watching the changes be applied in all other frames.

Label stationary objects with Merged Point Cloud2023-11-06T10:53:21+01:00

Make it easy to label stationary objects that only have a few points in each frame. Merged Point Cloud mode combines all the frames in a sequence into one large point cloud. By consolidating the points across frames, object outlines become more visible — allowing for precise annotation with a tight-fitting cuboid in a single go.

Propagate cuboids across sequences with machine learning2023-11-06T10:53:28+01:00

Speed up the process of 3D bounding box labeling with keyframe interpolation in the Batch Mode interface. On the Scale and Enterprise plans, you can also take advantage of the automated tracking feature.

Automated cuboid propagation lets you label a moving object once, then automatically track it across frames by hitting the “play” button. If you notice any deviations from the path, simply hit pause, make the necessary corrections, and then continue again.

Rotate a cuboid along all 3 axes2023-11-06T10:53:32+01:00

In the point cloud interface for cuboids, you have the option to enable full 3D rotation by adjusting the dataset settings. By rotating on three axes, you’ll get better control of its orientation for more precise, accurate, and efficient labeling.

Labeling on is so quick and simple. Plus, the documentation is very good. It does everything we need, so we don’t have to worry about it.

Libor Novak

Software Engineer

Auto-label and verify 2D images & sequences

Label and correct objects and regions quickly and accurately. Machine learning labeling models are here to help annotate objects with segmentation, bounding boxes, polygons, polylines, and keypoints.’s Superpixel tool for image segmentation boosts our labeling efficiency significantly. The workflows, Python SDK, and webhooks have been game changers for us. The platform is now seamlessly integrated with our active learning pipeline.

Andres Milioto

Senior Computer Vision Engineer
Author of SemanticKITTI and RangeNet++

We work with

Grow with us at every stage of your company, from POC to Production to Scale

Building your first datasets with a data-centric approach. Get started quickly with user-friendly tools and best practices included right out of the box.

Ideal for advanced setups requiring multi-sensor labeling. Ensure consistency and efficiency as you scale your data annotation processes.

Advanced automation and custom solutions. Achieve the perfect balance of security, customization, and flexibility for demanding data annotation tasks.

It’s the perfect platform for computer vision engineers and labeling teams

With reactive support and an easy-to-use API and interface, engineers around the world are using to get data streams labeled faster.

Autonomous robotics in a warehouse

Scale your labeling by outsourcing to someone you trust

Onboard your team onto, or outsource your labeling altogether. Whether you want to do quality control yourself — or use our white-glove service to be completely hands-off — there’s a labeling option for you.

Picture of someone labeling multi-sensor data on a point cloud

A few of the labeling agencies we work with

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Simplify multi-sensor labeling

Say hello to faster labeling, better quality data, and more time for your engineering team.
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