  In this paper we introduce Co-Fusion, a dense SLAM system that takes a live
stream of RGB-D images as input and segments the scene into different objects
(using either motion or semantic cues) while simultaneously tracking and
reconstructing their 3D shape in real time. We use a multiple model fitting
approach where each object can move independently from the background and still
be effectively tracked and its shape fused over time using only the information
from pixels associated with that object label. Previous attempts to deal with
dynamic scenes have typically considered moving regions as outliers, and
consequently do not model their shape or track their motion over time. In
contrast, we enable the robot to maintain 3D models for each of the segmented
objects and to improve them over time through fusion. As a result, our system
can enable a robot to maintain a scene description at the object level which
has the potential to allow interactions with its working environment; even in
the case of dynamic scenes.
