We consider the problem of aligning multiview scans obtained using
a range scanner. The computational pipeline for this problem can be
divided into two phases: (i) finding point-to-point correspondences
between overlapping scans, and (ii) registration of the scans based
on the found correspondences. The focus of this work is on global
registration in which the scans (modeled as point clouds) are required
to be jointly registered in a common reference frame. We consider
an optimization framework for global registration that is based on
rank-constrained semidefinite programming. We propose to solve
this semidefinite program using a non-convex variant of the ADMM
(Alternating Direction Method of Multipliers) algorithm. This results
in an efficient and scalable iterative method that requires just one
eigendecompostion per iteration. We present simulations results on
synthetic 3D models, using both clean and noisy correspondences.
An interesting finding is that the algorithm is robust to wrong corre-
spondences, namely, it yields high-quality reconstructions even when
a significant fraction of the correspondences are corrupted. Finally, by
using ICP to infer the correspondences, we present some promising
preliminary results for multiview reconstruction.