A Fast 3D Correspondence Method for Statistical Shape Modeling

Accurately identifying corresponded landmarks from a population of shape instances is the major challenge in constructing statistical shape models. In this paper, we address this landmark-based shape-correspondence problem for 3D cases by developing a highly efficient landmark-sliding algorithm. This algorithm is able to quickly refine all the landmarks in a parallel fashion by sliding them on the 3D shape surfaces. We use 3D thin-plate splines to model the shape-correspondence error so that the proposed algorithm is invariant to affine transformations and more accurately reflects the nonrigid biological shape deformations between different shape instances. In addition, the proposed algorithm can handle both open- and closed-surface shape, while most of the current 3D shape-correspondence methods can only handle genus-0 closed surfaces. We conduct experiments on 3D hippocampus data and compare the performance of the proposed algorithm to the state-of-the-art MDL and SPHARM methods. We...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Pahal Dalal, Brent C. Munsell, Song Wang, Jijun Tang, Kenton Oliver, Hiroaki Ninomiya, Xiangrong Zhou, Hiroshi Fujita
• Keywords: Biological Shape Deformations, Computer Vision, CVPR 2007, Efficient Landmark-sliding Algorithm, Shape Correspondence, Shape Surfaces, Statistical Shape Models