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

Robust Registration and Geometry Estimation from Unstructured Facial Scans