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The leader in autonomy data annotation
Collateral and Demos
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2D Instance Semantic Segmentation
2D instance semantic segmentation is an advanced computer vision technique that combines aspects of both semantic segmentation and instance segmentation. While semantic segmentation assigns a class label to each pixel in an image (e.g., road, vehicle, pedestrian), instance segmentation goes further by distinguishing between individual objects of the same class. This means not only knowing which pixels are "cars," but identifying specific cars as separate entities (Car 1, Car 2, etc.).
3D Semantic Segmentation
3D semantic segmentation is the process of assigning a semantic class (e.g., car, pedestrian, road, building, vegetation) to every point in a 3D point cloud. This technology has become essential for autonomous systems that need to understand their environment with precision and context.
User Docs
User Docs
Our User Documentation will provide important information on how to get started inside our Kognic platform, introductions to key concepts and detailed instructions on how to use our intuitive UI. We'll always post updates to new components being released, so bookmark this now!
API Docs
Developer - API Docs
Kognic provides HTTP APIs to our client-facing services and an API client library to wrap basic API calls to simplify calling from Python. We're constantly upgrading our API catalog, so check back often.
Help center
Help Center
Kognic is collecting the most common questions and topics from our users here. These include Support Guides for important processes that everyone needs to know. Take a read here - you can also find a link for Kognic Technical Support.
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ZOD - Zenseact Open Dataset
The Zenseact Open Dataset (ZOD) is a large multi-modal autonomous driving (AD) dataset, created by our customer, Zenseact. Over a 2-year period in 14 different European counties, researchers at Zenseact collected data using a fleet of vehicles equipped with a full sensor suite consisting of 3x lidars, 1x camera and IMU data. In total, the dataset includes 100k single frame images, 1473 sequences and 29 Drives.
Access ZOD
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Audi A2D2
The Zenseact Open Dataset (ZOD) is a large multi-modal autonomous driving (AD) dataset, created by our customer, Zenseact. Over a 2-year period in 14 different European counties, researchers at Zenseact collected data using a fleet of vehicles equipped with a full sensor suite consisting of 3x lidars, 1x camera and IMU data. In total, the dataset includes 100k single frame images, 1473 sequences and 29 Drives.
Access A2D2
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Berkeley BDD100K
This open source dataset is very diverse, including data from different times of the day, different weather scenarios that were captured from urban, highway and rural areas.
Access BDD100K
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iQmulus - TerraMobilita LiDAR
This database is produced in the framework of the iQmulus and TerraMobilita projects and integrated by Stereopolis II, a MLS system developed at the French National Mapping Agency (IGN). In this dataset, the entire 3D point cloud is segmented and classified within a dense urban environment.
Access iQmulus
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Image & video annotation services
This database is produced in the framework of the iQmulus and TerraMobilita projects and integrated by Stereopolis II, a MLS system developed at the French National Mapping Agency (IGN). In this dataset, the entire 3D point cloud is segmented and classified within a dense urban environment.
Access Nuscenes
