TY - GEN
T1 - Non-rigid point set registration
T2 - 20th Annual Conference on Neural Information Processing Systems, NIPS 2006
AU - Myronenko, Andriy
AU - Song, Xubo
AU - Carreira-Perpiñán, Miguel Á
PY - 2007
Y1 - 2007
N2 - We introduce Coherent Point Drift (CPD), a novel probabilistic method for nonrigid registration of point sets. The registration is treated as a Maximum Likelihood (ML) estimation problem with motion coherence constraint over the velocity field such that one point set moves coherently to align with the second set. We formulate the motion coherence constraint and derive a solution of regularized ML estimation through the variational approach, which leads to an elegant kernel form. We also derive the EM algorithm for the penalized ML optimization with deterministic annealing. The CPD method simultaneously finds both the non-rigid transformation and the correspondence between two point sets without making any prior assumption of the transformation model except that of motion coherence. This method can estimate complex non-linear non-rigid transformations, and is shown to be accurate on 2D and 3D examples and robust in the presence of outliers and missing points.
AB - We introduce Coherent Point Drift (CPD), a novel probabilistic method for nonrigid registration of point sets. The registration is treated as a Maximum Likelihood (ML) estimation problem with motion coherence constraint over the velocity field such that one point set moves coherently to align with the second set. We formulate the motion coherence constraint and derive a solution of regularized ML estimation through the variational approach, which leads to an elegant kernel form. We also derive the EM algorithm for the penalized ML optimization with deterministic annealing. The CPD method simultaneously finds both the non-rigid transformation and the correspondence between two point sets without making any prior assumption of the transformation model except that of motion coherence. This method can estimate complex non-linear non-rigid transformations, and is shown to be accurate on 2D and 3D examples and robust in the presence of outliers and missing points.
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M3 - Conference contribution
AN - SCOPUS:84864038736
SN - 9780262195683
T3 - Advances in Neural Information Processing Systems
SP - 1009
EP - 1016
BT - Advances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference
Y2 - 4 December 2006 through 7 December 2006
ER -