Flip-avoiding interpolating surface registration for skull reconstruction

Abstract

Reconstruction of skulls from defective models is a very important and challenging task in craniofacial surgery, forensics, and anthropology. Existing methods typically reconstruct approximating surfaces that regard corresponding points on the target skull as soft constraints, thus incurring non-zero error even for non-defective parts and high overall reconstruction error. This paper proposes a novel method that non-rigidly registers an interpolating surface that regards corresponding target points as hard constraints, thus achieving low reconstruction error. To overcome the shortcoming of interpolating surface, a flip-avoiding method is used to detect and exclude conflicting hard constraints that would otherwise cause surface patches to flip. Comprehensive test results show that our method is more accurate than existing methods and it is robust against severe outliers such as radiation artifacts in CT due to dental implants.