  With current trends in sensors (cheaper, more volume of data) and
applications (increasing affordability for new tasks, new ideas in what 3D data
could be useful for); there is corresponding increasing interest in the ability
to automatically, reliably, and cheaply, register together individual point
clouds. The volume of data to handle, and still elusive need to have the
registration occur fully reliably and fully automatically, mean there is a need
to innovate further. One largely untapped area of innovation is that of
exploiting the {\em semantic information} of the points in question. Points on
a tree should match points on a tree, for example, and not points on car.
Moreover, such a natural restriction is clearly human-like - a human would
generally quickly eliminate candidate regions for matching based on semantics.
Employing semantic information is not only efficient but natural. It is also
timely - due to the recent advances in semantic classification capabilities.
This paper advances this theme by demonstrating that state of the art
registration techniques, in particular ones that rely on "preservation of
length under rigid motion" as an underlying matching consistency constraint,
can be augmented with semantic information. Semantic identity is of course also
preserved under rigid-motion, but also under wider motions present in a scene.
We demonstrate that not only the potential obstacle of cost of semantic
segmentation, and the potential obstacle of the unreliability of semantic
segmentation; are both no impediment to achieving both speed and accuracy in
fully automatic registration of large scale point clouds.
