Constraint-Based Spectral Space Template Deformation for Ear Scans

Srinivasan Ramachandran, Tiberiu Popa, Eric Paquette

Keywords: Geometry Processing, Non-Rigid Deformation, Spectral Theory, Dense Registration, Laplacian Surface Editing

TL;DR: We use spectral mesh processing for a non-rigid deformation of a template ear to match a scanned ear with good semantic correspondence and without inheriting the noise.

Abstract: Ears are complicated shapes and contain a lot of folds. It is difficult to correctly deform an ear template to achieve the same shape as a scan while avoiding to reconstructing the noise from the scan and be robust to bad geometry found in the scan. We leverage the smoothness of the spectral space to help in the alignment of the semantic features of the ears. Edges detected in image space are used to identify relevant features from the ear that we align in the spectral representation by iteratively deforming the template ear. We then apply a novel reconstruction that preserves the deformation from the spectral space while reintroducing the original details. A final deformation based on constraints considering surface position and orientation deforms the template ear to match the shape of the scan. We tested our approach on many ear scans and observed that the resulting template shape provides a good compromise between complying to the shape of the scan and avoiding to reconstruct the noise found in the scan. Furthermore, our approach was robust to scan meshes exhibiting typical bad geometry such as cracks and handles.