Get consistent annotations across time and modalities
Label multiple sensors at the same time
Trusted by computer vision engineers across robotics and AV companies
Segments.ai’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.
Segments.ai 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
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.
Skip the back and forth between frames while making minor cuboid adjustments. Segments.ai lets you see all instances of an object through a sequence in a user-friendly, scrollable view. When you adjust a cuboid in one instance, it’ll update its position, dimensions, and rotations across frames.
If you want to take it a step further, you can use machine learning for auto-annotations of moving objects. You’ll speed up the process, but still retain complete manual oversight.
Need to label static objects with only a few points in each frame, but struggling to see the actual dimensions?
Segments.ai lets you unify all the frames in a sequence into a single merged point cloud. Outlines will be more visible, so the object can be annotated with a tight-fitting cuboid on the first attempt.
Accurate labels. Faster workflows.
Segments.ai helps machine learning teams and engineers label their data at scale (without worrying about quality).
Segments.ai 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.
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.
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.
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.
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 Segments.ai 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
Cartken
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.
With superpixels, your image is instantly divided into coherent regions. You can easily change the granularity of the superpixels by scrolling with your mouse, giving you precise control over the level of detail in your segmentation. Simply click on a superpixel to label the corresponding region.
Superpixel 2.0 goes beyond the color of nearby pixels. It also uses machine learning-assisted models – perfected for autonomous driving and robotics use cases – to recognize shapes. That means faster and more accurate segmentation of images, plus improved quality of labels.
Segment small objects and high-resolution images with the Autosegment tool. Just draw a box around the object you want to label, and it will automatically generate a segmentation mask for the foreground element in the box.
Built upon an expansive dataset of over 11 million images, the integrated Segment Anything Model lets you segment in a single click. Just hover your mouse over an object to see the suggested segmentation masks, then lock them in with the push of a button.
When your machine learning model is trained, you can run it on unlabeled images to generate label predictions. Then, you can upload these unlabeled images into Segments.ai for easy verification and corrections.
Segments.ai’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++
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.
With reactive support and an easy-to-use API and interface, engineers around the world are using Segments.ai to get data streams labeled faster.
Onboard your team onto Segments.ai, 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.
A few of the labeling agencies we work with
Justin Bériot2026-02-04T10:32:16+01:00February 4th, 2026|
Tracking attribute changes across sequences shouldn’t feel like detective work. Yet that’s been the reality. To check if attributes were correct, you opened frame 1, checked the sidebar, moved to [...]
Tom Staelens2026-02-04T10:46:37+01:00February 3rd, 2026|
Welcome to the Segments.ai monthly roundup. Last month, the autonomous vehicle landscape saw historic capital infusions and strategic pivots. Waymo secured a massive $16 billion funding round to fuel [...]
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