  Reconstructing dense, volumetric models of real-world 3D scenes is important
for many tasks, but capturing large scenes can take significant time, and the
risk of transient changes to the scene goes up as the capture time increases.
These are good reasons to want instead to capture several smaller sub-scenes
that can be joined to make the whole scene. Achieving this has traditionally
been difficult: joining sub-scenes that may never have been viewed from the
same angle requires a high-quality camera relocaliser that can cope with novel
poses, and tracking drift in each sub-scene can prevent them from being joined
to make a consistent overall scene. Recent advances, however, have
significantly improved our ability to capture medium-sized sub-scenes with
little to no tracking drift: real-time globally consistent reconstruction
systems can close loops and re-integrate the scene surface on the fly, whilst
new visual-inertial odometry approaches can significantly reduce tracking drift
during live reconstruction. Moreover, high-quality regression forest-based
relocalisers have recently been made more practical by the introduction of a
method to allow them to be trained and used online. In this paper, we leverage
these advances to present what to our knowledge is the first system to allow
multiple users to collaborate interactively to reconstruct dense, voxel-based
models of whole buildings using only consumer-grade hardware, a task that has
traditionally been both time-consuming and dependent on the availability of
specialised hardware. Using our system, an entire house or lab can be
reconstructed in under half an hour and at a far lower cost than was previously
possible.

Computer Vision and Pattern Recognition