  We present an open-source, real-time implementation of SemanticPaint, a
system for geometric reconstruction, object-class segmentation and learning of
3D scenes. Using our system, a user can walk into a room wearing a depth camera
and a virtual reality headset, and both densely reconstruct the 3D scene and
interactively segment the environment into object classes such as 'chair',
'floor' and 'table'. The user interacts physically with the real-world scene,
touching objects and using voice commands to assign them appropriate labels.
These user-generated labels are leveraged by an online random forest-based
machine learning algorithm, which is used to predict labels for previously
unseen parts of the scene. The entire pipeline runs in real time, and the user
stays 'in the loop' throughout the process, receiving immediate feedback about
the progress of the labelling and interacting with the scene as necessary to
refine the predicted segmentation.
