Get point cloud and image data labeled together
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
Label 3D point clouds (even if you only need 2D labels)
Annotate objects in point clouds 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 point cloud 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.
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
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.
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.
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++
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 Segments.ai to get data streams labeled faster.
Easy to set up and integrate
Clean documentation
Python SDK & API Keys
Integrate Segments.ai into your existing machine learning workflow using the simple-but-powerful Python SDK. You can build your tailored annotation workflow precisely the way you want.
Export to popular ML frameworks
Model-assisted labeling
A few of the labeling agencies we work with
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