Label comprehensive 3D scenes from LiDAR or RADAR sensors with additional photo and video context, AI object tracking and point cloud segmentation.
Annotation of point cloud scenes is not an easy task. Inspired by professional software like Blender, Supervisely offers user-friendly instruments to work with thousands of points.
Visualization and, especially, labeling of spatial point clouds is not a simple task. Apart from plain and well-understood image labeling, to successfully complete annotation project in 3D space we need to solve three additional challenges and provide:
Before you can identify and label an object of interest you need to see it clear from every angle. To let some sunlight into the scene, we have introduced widely known navigation from video games with WASD keyboard controls to move around point cloud and mouse to control the camera angle.
Along with additional viewports with top-side-front perspectives using orthographic projections, it gives accurate representation of what you are dealing with.
One cannot put a 3D box sitting behind a 2D monitor screen — that’s why having multiple viewports for editing a 3D box in multiple projections is a key to accurate annotation.
Provide more information for accurate labeling and identification with photo and video context. Supervisely automatically calculates correlation between 3D space and 2D context and projects your labeled objects on it, letting you achieve unprecedented quality of labeling.
In many task such as autonomous cars localization and mapping (SLAM) and lane detection you have to label not just a single point cloud, but a series of clouds, called episodes. Supervisely provides labeling toolbox specially designed for such a case.
Just like as specially designed video labeling toolbox is remarkably more performant than annotation of separate frames in image toolbox, specially designed 3D episodes toolbox is surpassingly more excellent in every aspect from playback speed to tracking performance.
It’s easy to get frustrated with hundreds of clouds and objects labeled.
Episode timeline panel provides overall structure, answers questions like what is already labeled and simplifies editing tag segments and tracked objects.
A common task is labeling of a moving object across multiple point clouds. Supervisely offers built-in AI tracking algorithms that automatically detects and tracks objects of various classes, as well as more classic linear interpolation and other methods of object tracking.
3D semantic segmentation of cloud points is quite challenging. But with the right tools Supervisely provides, classifying of even the most spatial point clouds becomes much easier.
Attach additional information to annotation objects or just tag specific segments of 3D point cloud episode with semantic, such as “what’s going on here”.
Object detection, classification and segmentation are not the only tasks solved with Supervisely — there is always more to it!
Because Supervisely is built like OS for computer vision, we made possible integration of the best machine learning models and tools on a single platform.
You will find a well-known projects from data science community, as well as our own Apps, providing a complete solution for entire AI development pipeline.
Configure every aspect of training from target classes to online augmentations, monitor metrics and terminal logs in real-time.
Dashboard to configure and monitor training
Understand how your model works on ground truth and new data and find how to correct negative output and increase performance.
Detailed statistics for all classes in images project
Generate synthetic datasets that drastically improve model results, especially when there is not enough ground truth.
Generate synthetic data: flying foregrounds on top of backgrounds
Perform all the necessary actions on your data, from importing and converting to skeletonization of masks and rasterization.
Configure, preview and split images and annotations with sliding window
Focus on labeling and let Supervisely do the rest.
Easy import and export, workspaces, backups, data insights and statistics
Teams with role-based access, issue tracking, comments, notifications
Avoid mistakes and don’t lose time with multi-level quality assurance process
Avoid mistakes and don’t lose time with multi-level quality assurance process
Collaborate together with your team to transform existing assets into labeled data, trained models and deployed solutions. Supervisely helps to organize work of different professionals: labelers, data scientists, domain experst, managers and engineers.
A quick overview from our founder
The list of features does not end here as we constantly extend Supervisely with applications, that improve both labeling and management of point clouds.
Learn about Supervisely Apps
Converts KITTI 3D format to Supervisely pointcloud format
Export project or dataset in Supervisely pointcloud episode format
Constantly growing ecosystem of hundreds of ready-to-use apps that extend and add new functionality.
Learn about EcosystemImpressed by what you just read, but want to label other type of data? Check what else we have!
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We use Supervisely since 2019. The key advantage of this tool is that Supervisely provides a complete data treatment pipeline. An important advantage is that a Supervisely instance can be deployed autonomously on a Client infrastructure, and distributed on different servers.
It helps to treat enterprise’s internal and often confidential data in a secured way. Together with a user-friendly interface, a clear documentation and a friendly and reactive support team it helps us to do Data Scientist work better and faster.
BMW Group is using the Supervise.ly solution to create automated verifications for ensuring a very high product quality across the whole production chain in vehicle and vehicle component manufacturing.
BMW Group uses Supervise.ly to annotate manufacturing images from production lines in their world-wide plants for enhancing quality inspections using deep learning. The Supervise.ly tooling also supports the process for continuously updating AI models using semi-automated labeling.
Supervise.ly is integrated into the BMW Group AI Platform in order to empower computer vision based AI use cases.
We’ve been working together with Supervisely since 2020, and we have helped each other to grow rapidly and significantly.
Supervisely’s team has been incredibly fast and agile in taking on board our requirements and implementing useful, up-to-date computer vision functionalities. In addition, we appreciate the openness and scalability of their ecosystem combined with the Python SDK and API. So far, we have been very satisfied with the platform and the incredibly responsive team.
Working with Supervise.ly has significantly enhanced our capability to develop AI models for lung CT scans. What sets Supervise.ly apart is its exceptional support team who are really responsive and adapt to our unique needs with a range of apps and helper files.
Their team has developed updates driven by our specific user feedback, making Supervise.ly a critical component of our research ecosystem in generating the specific labels we need to provide AI-driven solutions. We are immensely grateful for their pivotal role in our work.
We originally set out to look for tools that could help us with data annotation, and we discovered that Supervisely excels at that and much more. It has become an integral part of our workflow in annotation, model training, and evaluation.
We've been exceedingly impressed with the customer support, addition of new features, and the flexibility of the publicly available SDK/API. The Supervisely team has also been fast to respond to support questions, and has shown a lot of openness when given feedback on potential improvements.
We have been using Supervisely for a few years now to help label and organize our data for AI training. The interface is user-friendly and the tools are intuitive to use, which has made the annotation process much more efficient for our team. We run Supervisely locally, which allows us to stay in control of our data. We also use Supervisely for annotation reviews, and the review tools have been invaluable in ensuring the quality and accuracy. The Python SDK has also been incredibly helpful in automating and streamlining our workflow. In addition, the support team on Slack has been extremely helpful and responsive. The ability to collaborate with my colleagues on the same project has also been a huge time-saver.
Overall, we have been extremely satisfied with Supervisely and would highly recommend it to anyone in need of a reliable and efficient annotation solution.
Supervisely provides first-rate experience since 2017, longer than most of computer vision platforms over there.
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