This paper describes a parallel implementation developed to improve the time performance of the Iterative Closest Point Algorithm. Within each iteration, the correspon-dence calculations are distributed among the processor re-sources. At the end of each iteration, the results of the cor

The problem of geometric alignment of two roughly preregistered, partially overlapping, rigid, noisy 3D point sets is considered. A new natural and simple, robustified extension of the popular Iterative Closest Point (ICP) algorithm [1] is presented, called the Trimmed ICP (TrICP). The new

. This paper describes a method for accurate and computationally efficient registration of 3\GammaD shapes including curves and surfaces. The method is based on the iterative closest point (ICP) algorithm. The real strength of our algorithm is the use of Morphological Voronoi tessellation method

Abstract — We propose a novel method to enhance a family of ICP(iterative closest point) algorithms by updating velocity. Even though ICP algorithms play a dominant role in a model based tracking, it is difficult to avoid an accumulated tracking error during a continuous motion. It is because

This paper describes an approximately expectationmaximization (EM) formulation of a homographical iterative closest point registration approach (henceforth HICP). We show that such an EM approach allows the algorithm to converge faster, and more robustly in the presence of noise. Although

Abstract. The Iterative closest point (ICP) algorithm is a widely used method for 3D point set registration. It iteratively establishes point correspondences between two input data sets and computes a rigid transformation accordingly. From a statistical point of view, the algorithm implicitly

Abstract—Motivated by the problem of retinal image registration, this paper introduces and analyzes a new registration algorithm called Dual-Bootstrap Iterative Closest Point (Dual-Bootstrap ICP). The approach is to start from one or more initial, low-order estimates that are only accurate in small

This paper presents a novel approach for registering two sets of 3-D range data points, using an optimization algorithm that is both robust and efficient. The algorithm combines the speed of an iterative closest point algorithm with the robustness of a simulated annealing algorithm. Additionally, a

and the Iterative Closest Point algorithm are employed to establish correspondence between test and target and to compensate for the non-rigid nature of the surfaces. Statistical modelling in the form of Gaussian Mixture Models is used to parameterise the distribution of errors in facial surfaces after registration

registration technique (e.g., the Iterative Closest Point algorithm). In this paper, we propose a 3D face tracker that is based on appearance registration and on a fast variant of a robust Iter-ative Closest Point algorithm. The resulting 3D face tracker combines the advantages of both appearance