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Labeling objects in point clouds and images is no easy job, especially if you need consistent object IDs across time and sensors. We’re excited to announce our new multi-sensor labeling interface, which makes this much easier.
Multi-sensor datasets
Where you previously had to create separate datasets for your 3D point cloud and 2D image labeling, we now also offer a new multi-sensor dataset where you can label all your sensor data in a single labeling interface. This brings several advantages:
- Objects can be labeled with consistent object IDs across sensors. For example, if you have a robot with one lidar and 4 cameras, an object annotated with a 3D cuboid in the point cloud view can be given the same object ID as the same car annotated with a 2D bounding box in the 4 images.
- A single person can label the data from all lidar and camera sensors of a recording, instead of it being split up across multiple labelers working in separate datasets. This makes for a more efficient and consistent labeling process for multi-sensor data.
- You can leverage the 3D annotations to drastically speed up the 2D labeling. More about this in our next newsletter!
To set up a multi-sensor dataset, check out our docs on the required data format.
Image sequence segmentation
You could already label single images with bitmap segmentation labels, but we now also launched our labeling interface for segmenting image sequences. This interface is still in beta, so please reach out to us if you encounter any bugs or performance issues. More updates are coming soon.
A look behind the scenes
Curious how we built our synced camera feature with support for fisheye distortions? Check out our blog post on Simulating Real Cameras using Three.js for a technical look behind the scenes.
