3D craniofacial registration using thin-plate spline transform and cylindrical surface projection

Abstract

Craniofacial registration is used to establish the point-to-point correspondence in a unified coordinate system among human craniofacial models. It is the foundation of craniofacial reconstruction and other craniofacial statistical analysis research. In this paper, a non-rigid 3D craniofacial registration method using thin-plate spline transform and cylindrical surface projection is proposed. First, the gradient descent optimization is utilized to improve a cylindrical surface fitting (CSF) for the reference craniofacial model. Second, the thin-plate spline transform (TPST) is applied to deform a target craniofacial model to the reference model. Finally, the cylindrical surface projection (CSP) is used to derive the point correspondence between the reference and deformed target models. To accelerate the procedure, the iterative closest point ICP algorithm is used to obtain a rough correspondence, which can provide a possible intersection area of the CSP. Finally, the inverse TPST is used to map the obtained corresponding points from the deformed target craniofacial model to the original model, and it can be realized directly by the correspondence between the original target model and the deformed target model. Three types of registration, namely, reflexive, involutive and transitive registration, are carried out to verify the effectiveness of the proposed craniofacial registration algorithm. Comparison with the methods in the literature shows that the proposed method is more accurate.

GRÖMeR: A Pipeline for Geodesic Refinement of Mesh Registration

“…Many examples of similar surface-to-surface registration problems exist, in which one must align a template model to a sub-sampled and distorted specific shape. Various registration approaches have been employed to address such problems, such as rigid or affine iterative closest points (ICP), thin plate splines, model based, Procrustes, and combinations of these methods [3,4,7,9,10]. Each approach and the resulting software pipelines have limitations, often with small numbers of correspondence points and the ambiguities of non-rigid deformation [2,3].…”

3D Face Model Super-Resolution Based on Radial Curve Estimation

“…The methods in [9,10] use mesh simplification and down-sampling of high-resolution 3D face models to produce low-resolution models. They only need to perform data registration among high-resolution 3D face models, which can be realized easily by many existing methods [13]. We also tried to use the registration method [13] to establish the point correspondence among low resolution 3D face models.…”

“…They only need to perform data registration among high-resolution 3D face models, which can be realized easily by many existing methods [13]. We also tried to use the registration method [13] to establish the point correspondence among low resolution 3D face models. As a result, the registration accuracy was very bad.…”

“…Thus, we have to locate the nose tip point and the symmetrical plane of a 3D face model for radial curve extraction. Assuming 3D face models are triangle mesh models, we first perform principal component analysis for the vertices of the face model to establish an initial coordinate system, i.e., three principal orientations as the coordinate axes with Y axis throughout the front and back of the face, and then fit a cylinder to the 3D face model [13] and adjust the Z axis to be parallel to the cylinder's axis. The nose tip is the most protruding point on the face's surface, so the point of the maximal value in Y direction is chosen as the nose tip.…”

“…Therefore, we obtained a group of landmark correspondences between the estimated high resolution face model and the reference face model. Using these landmark correspondences as control points, we deformed the reference face to obtain the high resolution face models by using Thin Plate Splines (TPS) deformation [13]. In this research, we used face models from 111 people.…”