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98
Efficient Variants of the ICP Algorithm
 INTERNATIONAL CONFERENCE ON 3D DIGITAL IMAGING AND MODELING
, 2001
"... The ICP (Iterative Closest Point) algorithm is widely used for geometric alignment of threedimensional models when an initial estimate of the relative pose is known. Many variants of ICP have been proposed, affecting all phases of the algorithm from the selection and matching of points to the minim ..."
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Cited by 694 (5 self)
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The ICP (Iterative Closest Point) algorithm is widely used for geometric alignment of threedimensional models when an initial estimate of the relative pose is known. Many variants of ICP have been proposed, affecting all phases of the algorithm from the selection and matching of points to the minimization strategy. We enumerate and classify many of these variants, and evaluate their effect on the speed with which the correct alignment is reached. In order to improve convergence for nearlyflat meshes with small features, such as inscribed surfaces, we introduce a new variant based on uniform sampling of the space of normals. We conclude by proposing a combination of ICP variants optimized for high speed. We demonstrate an implementation that is able to align two range images in a few tens of milliseconds, assuming a good initial guess. This capability has potential application to realtime 3D model acquisition and modelbased tracking.
Iterative point matching for registration of freeform curves and surfaces
, 1994
"... A heuristic method has been developed for registering two sets of 3D curves obtained by using an edgebased stereo system, or two dense 3D maps obtained by using a correlationbased stereo system. Geometric matching in general is a difficult unsolved problem in computer vision. Fortunately, in ma ..."
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Cited by 655 (7 self)
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A heuristic method has been developed for registering two sets of 3D curves obtained by using an edgebased stereo system, or two dense 3D maps obtained by using a correlationbased stereo system. Geometric matching in general is a difficult unsolved problem in computer vision. Fortunately, in many practical applications, some a priori knowledge exists which considerably simplifies the problem. In visual navigation, for example, the motion between successive positions is usually approximately known. From this initial estimate, our algorithm computes observer motion with very good precision, which is required for environment modeling (e.g., building a Digital Elevation Map). Objects are represented by a set of 3D points, which are considered as the samples of a surface. No constraint is imposed on the form of the objects. The proposed algorithm is based on iteratively matching points in one set to the closest points in the other. A statistical method based on the distance distribution is used to deal with outliers, occlusion, appearance and disappearance, which allows us to do subsetsubset matching. A leastsquares technique is used to estimate 3D motion from the point correspondences, which reduces the average distance between points in the two sets. Both synthetic and real data have been used to test the algorithm, and the results show that it is efficient and robust, and yields an accurate motion estimate.
Fast and Globally Convergent Pose Estimation From Video Images
, 1998
"... Determining the rigid transformation relating 2D images to known 3D geometry is a classical problem in photogrammetry and computer vision. Heretofore, the best methods for solving the problem have relied on iterative optimization methods which cannot be proven to converge and/or which do not effecti ..."
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Cited by 149 (6 self)
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Determining the rigid transformation relating 2D images to known 3D geometry is a classical problem in photogrammetry and computer vision. Heretofore, the best methods for solving the problem have relied on iterative optimization methods which cannot be proven to converge and/or which do not effectively account for the orthonormal structure of rotation matrices. We show that the pose estimation problem can be formulated as that of minimizing an error metric based on collinearity in object (as opposed to image) space. Using object space collinearity error, we derive an iterative algorithm which directly computes orthogonal rotation matrices and which is globally convergent. Experimentally, we show that the method is computationally efficient, that it is no less accurate than the best currently employed optimization methods, and that it outperforms all tested methods in robustness to outliers. ChienPing Lu, Silicon Graphics Inc. cplu@engr.sgi.com y Greg Hager, Department of Computer...
New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence
"... A fundamental open problem in computer visiondetermining pose and correspondence between two sets of points in spaceis solved with a novel, fast [O(nm)], robust and easily implementable algorithm. The technique works on noisy 2D or 3D point sets that may be of unequal sizes and may differ by n ..."
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Cited by 102 (20 self)
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A fundamental open problem in computer visiondetermining pose and correspondence between two sets of points in spaceis solved with a novel, fast [O(nm)], robust and easily implementable algorithm. The technique works on noisy 2D or 3D point sets that may be of unequal sizes and may differ by nonrigid transformations. Using a combination of optimization techniques such as deterministic annealing and the softassign, which have recently emerged out of the recurrent neural network/statistical physics framework, analog objective functions describing the problems are minimized. Over thirty thousand experiments, on randomly generated points sets with varying amounts of noise and missing and spurious points, and on handwritten character sets demonstrate the robustness of the algorithm. Keywords: Pointmatching, pose estimation, correspondence, neural networks, optimization, softassign, deterministic annealing, affine. 1 Introduction Matching the representations of two images has long...
An Algorithmic Overview of Surface Registration . . .
 MEDICAL IMAGE ANALYSIS
, 2000
"... This paper presents a literature survey of automatic 3D surface registration techniques emphasizing the mathematical and algorithmic underpinnings of the subject. The relevance of surface registration to medical imaging is that there is much useful anatomical information in the form of collected ..."
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Cited by 88 (1 self)
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This paper presents a literature survey of automatic 3D surface registration techniques emphasizing the mathematical and algorithmic underpinnings of the subject. The relevance of surface registration to medical imaging is that there is much useful anatomical information in the form of collected surface points which originate from complimentary modalities and which must be reconciled. Surface registration
A comparison of four algorithms for estimating 3d rigid transformations
 In Proc. British Machine Vision Conference
, 1995
"... A common need in machine vision is to compute the 3D rigid transformation that exists between two sets of points for which corresponding pairs have been determined. In this paper a comparative analysis of four popular and efficient algorithms is given. Each computes the translational and rotational ..."
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Cited by 74 (1 self)
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A common need in machine vision is to compute the 3D rigid transformation that exists between two sets of points for which corresponding pairs have been determined. In this paper a comparative analysis of four popular and efficient algorithms is given. Each computes the translational and rotational components of the transform in closedform as the solution to a least squares formulation of the problem. They differ in terms of the representation of the transform and the method of solution, using respectively: singular value decomposition of a matrix, orthonormal matrices, unit quaternions and dual quaternions. This comparison presents results of several experiments designed to determine the (1) accuracy in the presence of noise, (2) stability with respect to degenerate data sets, and (3) relative computation time of each approach. 1
Object Pose from 2D to 3D Point and Line Correspondences
, 1995
"... In this paper we present a method for optimally estimating the rotation and translation between a camera and a 3D object from point and/or line correspondences. First we devise an error function and second weshowhowto minimize this error function. The quadratic nature of this function is made poss ..."
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Cited by 59 (10 self)
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In this paper we present a method for optimally estimating the rotation and translation between a camera and a 3D object from point and/or line correspondences. First we devise an error function and second weshowhowto minimize this error function. The quadratic nature of this function is made possible by representing rotation and translation with a dual number quaternion. We provide a detailed account of the computational aspects of a trustregion optimization method. This method compares favourably with Newton's method which has extensively been used to solve the problem at hand, with FaugerasToscani's linear method [6] for calibrating a camera, and with the LevenbergMarquardt nonlinear optimization method. Finally we present some experimental results which demonstrate the robustness of our method with respect to image noise and matching errors.
A Robust Point Matching Algorithm for Autoradiograph Alignment
, 1997
"... We present a novel method for the geometric alignment of autoradiographs of the brain. The method is based on finding the spatial mapping and the onetoone correspondences (or homologies) between point features extracted from the images and rejecting nonhomologies as outliers. In this way, we atte ..."
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Cited by 47 (12 self)
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We present a novel method for the geometric alignment of autoradiographs of the brain. The method is based on finding the spatial mapping and the onetoone correspondences (or homologies) between point features extracted from the images and rejecting nonhomologies as outliers. In this way, we attempt to account for the local natural and artifactual differences between the autoradiograph slices. We have executed the resulting automated algorithm on a set of left prefrontal cortex autoradiograph slices, specifically demonstrated its ability to perform point outlier rejection, validated it using synthetically generated spatial mappings and provided a visual comparison against the well known iterated closest point (ICP) algorithm. Visualization of a stack of aligned left prefrontal cortex autoradiograph slices is also provided.
Stereobased egomotion estimation using pixel tracking and iterative closest point
 in IEEE International Conference on Computer Vision Systems
, 2006
"... In this paper, we present a stereovision algorithm for realtime 6DoF egomotion estimation, which integrates image intensity information and 3D stereo data in the wellknown Iterative Closest Point (ICP) scheme. The proposed method addresses a basic problem of standard ICP, i.e. its inability to pe ..."
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Cited by 27 (0 self)
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In this paper, we present a stereovision algorithm for realtime 6DoF egomotion estimation, which integrates image intensity information and 3D stereo data in the wellknown Iterative Closest Point (ICP) scheme. The proposed method addresses a basic problem of standard ICP, i.e. its inability to perform the segmentation of data points and to deal with large displacements. Neither apriori knowledge of the motion nor inputs from other sensors are required, while the only assumption is that the scene always contains visually distinctive features which can be tracked over subsequent stereo pairs. This generates what is usually called Visual Odometry. The paper details the various steps of the algorithm and presents the results of experimental tests performed with an allterrain mobile robot, proving the method to be as accurate as effective for autonomous navigation purposes. 1.
W.Y.: On Pose Recovery for Generalized Visual Sensors
 IEEE Transactions on Pattern Analysis and Machine Intelligence
"... Abstract—With the advances in imaging technologies for robot or machine vision, new imaging devices are being developed for robot navigation or imagebased rendering. However, to satisfy some design criterion, such as image resolution or viewing ranges, these devices are not necessarily being design ..."
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Cited by 25 (3 self)
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Abstract—With the advances in imaging technologies for robot or machine vision, new imaging devices are being developed for robot navigation or imagebased rendering. However, to satisfy some design criterion, such as image resolution or viewing ranges, these devices are not necessarily being designed to follow the perspective rule and, thus, the imaging rays may not pass through a common point. Such generalized imaging devices may not be perspective and, therefore, their poses cannot be estimated with traditional techniques. In this paper, we propose a systematic method for pose estimation of such a generalized imaging device. We formulate it as a nonperspective n point (NPnP) problem. The case with exact solutions, n 3, is investigated comprehensively. Approximate solutions can be found for n>3 in a leastsquarederror manner by combining an initialposeestimation procedure and an orthogonally iterative procedure. This proposed method can be applied not only to nonperspective imaging devices but also perspective ones. Results from experiments show that our approach can solve the NPnP problem accurately. Index Terms—Computer vision, camera pose estimation, generalized imaging device (GID), perspective n point problem (PnP), nonperspective n point problem (NPnP). æ 1