  Commercial off the shelf (COTS) 3D scanners are capable of generating point
clouds covering visible portions of a face with sub-millimeter accuracy at
close range, but lack the coverage and specialized anatomic registration
provided by more expensive 3D facial scanners. We demonstrate an effective
pipeline for joint alignment of multiple unstructured 3D point clouds and
registration to a parameterized 3D model which represents shape variation of
the human head. Most algorithms separate the problems of pose estimation and
mesh warping, however we propose a new iterative method where these steps are
interwoven. Error decreases with each iteration, showing the proposed approach
is effective in improving geometry and alignment. The approach described is
used to align the NDOff-2007 dataset, which contains 7,358 individual scans at
various poses of 396 subjects. The dataset has a number of full profile scans
which are correctly aligned and contribute directly to the associated mesh
geometry. The dataset in its raw form contains a significant number of
mislabeled scans, which are identified and corrected based on alignment error
using the proposed algorithm. The average point to surface distance between the
aligned scans and the produced geometries is one half millimeter.
