In this paper, we propose a new framework to perform nonrigid surface registration. It is based on various extensions of an iterative algorithm recently presented by several researchers (Besl and McKay, 1992; Champleboux et al., 1992; Chen and Medioni, 1992; Menq and Lai, 1992; Zhang, 1994) to rigidly register surfaces represented by a set of 3D points, when a prior estimate of the displacement is available. Our framework consists of three stages: • First, we search for the best rigid displacement to superpose the two surfaces. We show how to efficiently use curvatures to superpose principal frames at possible corresponding points in order to find a prior rough estimate of the displacement and initialize the iterative algorithm. • Second, we search for the best affine transformation. We introduce differential information in points coordinates: this allows us to match locally similar points. Then, we show how principal frames and curvatures are transformed by an affine transformation. Finally, we introduce this differential information in a global criterion minimized by extended Kalman filtering in order to ensure the convergence of the algorithm. • Third, we locally deform the surface. Instead of computing a global affine transformation, we attach to each point a local attine transformation varying smoothly along the surface. We call this deformation a locally affine deformation. All these stages are illustrated with experiments on various real biomedical surfaces (teeth, faces, skulls, brains and hearts), which demonstrate the validity of the approach.
Feldmar and Ayache REF ) perform a surface registration based on a distance measure including local geometrical properties of the surfaces.
Rigid, affine and locally affine registration of free-form surfaces
International Journal of Computer Vision
International Journal of Computer Vision
Computer Science