  We present an accurate, robust and fast method for registration of 3D scans.
Our motion estimation optimizes a robust cost function on the intrinsic
representation of rigid motions, i.e., the Special Euclidean group
$\mathbb{SE}(3)$. We exploit the geometric properties of Lie groups as well as
the robustness afforded by an iteratively reweighted least squares
optimization. We also generalize our approach to a joint multiview method that
simultaneously solves for the registration of a set of scans. We demonstrate
the efficacy of our approach by thorough experimental validation. Our approach
significantly outperforms the state-of-the-art robust 3D registration method
based on a line process in terms of both speed and accuracy. We also show that
this line process method is a special case of our principled geometric
solution. Finally, we also present scenarios where global registration based on
feature correspondences fails but multiview ICP based on our robust motion
estimation is successful.
