After the 3D predictions are made, the next step is to manually verify and correct them. Humans are required for this task and need to be equipped with the right tools to interact with the data and machine-generated predictions efficiently. To make labeling point cloud data easier, we’ve developed several features.
The synced camera feature is the first one. It shows the camera image corresponding to where the mouse pointer is in the 3D space, making it easier to identify objects of interest.
Another handy feature is the batch mode, which allows quick adjustments to a particular object track’s cuboid throughout the sequence.
With ML-assisted cuboid propagation mode for labeling moving objects and a merged point cloud mode for labeling static objects that aren’t moving throughout the sequence.
These features make labeling more effortless. We’re also continuously working on new tools, such as the ability to automatically highlight potential labeling mistakes.