Optimal Step Nonrigid ICP Algorithms for Surface Registration

We show how to extend the ICP framework to nonrigid registration, while retaining the convergence properties of the original algorithm. The resulting optimal step nonrigid ICP framework allows the use of different regularisations, as long as they have an adjustable stiffness parameter. The registration loops over a series of decreasing stiffness weights, and incrementally deforms the template towards the target, recovering the whole range of global and local deformations. To find the optimal deformation for a given stiffness, optimal iterative closest point steps are used. Preliminary correspondences are estimated by a nearest-point search. Then the optimal deformation of the template for these fixed correspondences and the active stiffness is calculated. Afterwards the process continues with new correspondences found by searching from the displaced template vertices. We present an algorithm using a locally affine regularisation which assigns an affine transformation to each vertex and minimises the difference in the transformation of neighbouring vertices. It is shown that for this regularisation the optimal deformation for fixed correspondences and fixed stiffness can be determined exactly and efficiently. The method succeeds for a wide range of initial conditions, and handles missing data robustly. It is compared qualitatively and quantitatively to other algorithms using synthetic examples and real world data.

[1]  L. Dekker Hybrid computation in mathematical programming , 1973, Math. Program..

[2]  Gérard G. Medioni,et al.  Object modeling by registration of multiple range images , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[3]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Andrea J. van Doorn,et al.  Surface shape and curvature scales , 1992, Image Vis. Comput..

[5]  R. Szeliski Matching 3-D Anatomical Surfaces with Non-Rigid Deformations , 2020 .

[6]  Jorge Nocedal,et al.  Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization , 1997, TOMS.

[7]  Michael J. Todd,et al.  Mathematical programming , 2004, Handbook of Discrete and Computational Geometry, 2nd Ed..

[8]  Ron Kikinis,et al.  3D Image Matching Using a Finite Element Based Elastic Deformation Model , 1999, MICCAI.

[9]  Matthew Turk,et al.  A Morphable Model For The Synthesis Of 3D Faces , 1999, SIGGRAPH.

[10]  Hans-Peter Seidel,et al.  Head shop: generating animated head models with anatomical structure , 2002, SCA '02.

[11]  Steve Marschner,et al.  Filling holes in complex surfaces using volumetric diffusion , 2002, Proceedings. First International Symposium on 3D Data Processing Visualization and Transmission.

[12]  Zoran Popovic,et al.  The space of human body shapes: reconstruction and parameterization from range scans , 2003, ACM Trans. Graph..

[13]  Nicholas Ayache,et al.  Rigid, affine and locally affine registration of free-form surfaces , 1996, International Journal of Computer Vision.

[14]  Timothy A. Davis,et al.  Algorithm 832: UMFPACK V4.3---an unsymmetric-pattern multifrontal method , 2004, TOMS.

[15]  Richard Szeliski,et al.  Matching 3-D anatomical surfaces with non-rigid deformations using octree-splines , 1993, Proceedings of IEEE Workshop on Biomedical Image Analysis.

[16]  Jan Modersitzki,et al.  Curvature Based Image Registration , 2004, Journal of Mathematical Imaging and Vision.