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24
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 447 (4 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.
Multiview Registration for Large Data Sets
, 1999
"... In this paper we present a multiview registration method for aligning range data. We first align scans pairwise with each other and use the pairwise alignments as constraints that the multiview step enforces while evenly diffusing the pairwise registration errors. This approach is especially suitabl ..."
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Cited by 178 (1 self)
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In this paper we present a multiview registration method for aligning range data. We first align scans pairwise with each other and use the pairwise alignments as constraints that the multiview step enforces while evenly diffusing the pairwise registration errors. This approach is especially suitable for registering large data sets, since using constraints from pairwise alignments does not require loading the entire data set into memory to perform the alignment. The alignment method is efficient, and it is less likely to get stuck into a local minimum than previous methods, and can be used in conjunction with any pairwise method based on aligning overlapping surface sections.
Least squares 3D surface and curve matching
 ISPRS Journal of Photogrammetry and Remote Sensing
, 2005
"... The automatic coregistration of point clouds, representing 3D surfaces, is a relevant problem in 3D modeling. This multiple registration problem can be defined as a surface matching task. We treat it as least squares matching of overlapping surfaces. The surface may have been digitized/sampled poin ..."
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Cited by 60 (13 self)
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The automatic coregistration of point clouds, representing 3D surfaces, is a relevant problem in 3D modeling. This multiple registration problem can be defined as a surface matching task. We treat it as least squares matching of overlapping surfaces. The surface may have been digitized/sampled point by point using a laser scanner device, a photogrammetric method or other surface measurement techniques. Our proposed method estimates the transformation parameters of one or more 3D search surfaces with respect to a 3D template surface, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between the surfaces. This formulation gives the opportunity of matching arbitrarily oriented 3D surface patches. It fully considers 3D geometry. Besides the mathematical model and execution aspects we address the further extensions of the basic model. We also show how this method can be used for curve matching in 3D space and matching of curves to surfaces. Some practical examples based on the registration of closerange laser scanner and photogrammetric point clouds are presented for the demonstration of the method. This surface matching technique is a generalization of the least squares image matching concept and offers high flexibility for any kind of 3D surface correspondence problem, as well as statistical tools for the analysis of the quality of final matching results.
Registration of 3D Partial Surface Models Using Luminance and Depth Information
 In Proc. Int. Conf. on Recent Advances in 3D Digital Imaging and Modeling
, 1997
"... Textured surface models of threedimensional objects are gaining importance in computer graphics applications. These models often have to be merged from several overlapping partial models which have to be registered (i.e. the relative transformation between the partial models has to be determined) p ..."
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Cited by 37 (0 self)
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Textured surface models of threedimensional objects are gaining importance in computer graphics applications. These models often have to be merged from several overlapping partial models which have to be registered (i.e. the relative transformation between the partial models has to be determined) prior to the merging process. In this paper a method is presented that makes use of both camerabased depth information (e.g. from stereo) and the luminance image. The luminance information is exploited to determine corresponding point sets on the partial surfaces using an optical flow approach. Quaternions are then employed to determine the transformation between the partial models which minimizes the sum of the 3D Euclidian distances between the corresponding point sets. In order to find corresponding points on the partial surfaces luminance information is linearized. The procedure is iterated until convergence is reached. In contrast to only using depth information, employing luminance sp...
Robust Simultaneous Registration of Multiple Range Images
, 2002
"... The registration problem of multiple range images is fundamental for many applications that rely on precise geometric models. We propose a robust registration method that can align multiple range images comprised of a large number of data points. The proposed method minimizes an error function that ..."
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Cited by 35 (7 self)
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The registration problem of multiple range images is fundamental for many applications that rely on precise geometric models. We propose a robust registration method that can align multiple range images comprised of a large number of data points. The proposed method minimizes an error function that is constructed to be global against all range images, providing the ability to diffusively distribute errors instead of accumulating them. The minimization strategy is designed to be efficient and robust against outliers by using conjugate gradient search utilizing Mestimator. Also, for "better" point correspondence search, the laser reflectance strength is used as an additional attribute of each 3D data point. For robustness against data noise, the framework is designed not to use secondary information, i.e. surface normals, in its error metric. We describe the details of the proposed method, and present experimental results applying the proposed method to real data.
Surface Reconstruction and Display from Range and Color Data
, 1997
"... This dissertation addresses the problem of scanning both the color and geometry of real objects and displaying realistic images of the scanned objects from arbitrary viewpoints. We present a complete system that uses a stereo camera system with active lighting to scan the object surface geometry and ..."
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Cited by 33 (5 self)
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This dissertation addresses the problem of scanning both the color and geometry of real objects and displaying realistic images of the scanned objects from arbitrary viewpoints. We present a complete system that uses a stereo camera system with active lighting to scan the object surface geometry and color as visible from one point of view. Scans expressed in sensor coordinates are registered into a single objectcentered coordinate system by aligning both the color and geometry where the scans overlap. The range data are integrated into a surface model using a robust hierarchical space carving method. The fit of the resulting approximate mesh to data is improved and the mesh structure is simplified using mesh optimization methods. In addition, two methods are developed for viewdependent display of the reconstructed...
A Method for the Registration of Attributed Range Images
 Int. Conf. on 3D Imaging and Modeling, Quebec, May 28
, 2001
"... Registration of range images requires the identification of common portions of surfaces between which a distance minimization is performed. This paper proposes a framework for the use of dense attributes of range image elements as a matching constraint in the registration. These attributes are chose ..."
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Cited by 28 (1 self)
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Registration of range images requires the identification of common portions of surfaces between which a distance minimization is performed. This paper proposes a framework for the use of dense attributes of range image elements as a matching constraint in the registration. These attributes are chosen to be invariant to rigid transformations, so that their value is similar in different views of the same surface portion. Attributes can be derived from the geometry information in the range image, such as surface curvature, or be obtained from associated intensity measurements. The method is based on the Iterative Closest Compatible Point algorithm augmented with a random sampling scheme that uses the distribution of attributes as a guide for point selection. Distance minimization is performed only between pairs of points considered compatible on the basis of their attributes. The performance of the method is illustrated on a rotationally symmetric object with color patterns.
Least squares 3D surface matching
 IAPRS, 34(5/W16), (on CDROM
, 2004
"... The automatic coregistration of point clouds, representing 3D surfaces, is a relevant problem in 3D modeling. This registration problem can be defined as a surface matching problem. We treat it as least squares matching of overlapping surfaces. The point cloud may have been digitized/sampled point ..."
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Cited by 20 (4 self)
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The automatic coregistration of point clouds, representing 3D surfaces, is a relevant problem in 3D modeling. This registration problem can be defined as a surface matching problem. We treat it as least squares matching of overlapping surfaces. The point cloud may have been digitized/sampled point by point using a laser scanner device, a photogrammetric method or other surface measurement techniques. In the past, several efforts have been made concerning the registration of 3D point clouds. One of the most popular methods is the Iterative Closest Point (ICP) algorithm. Several variations and improvements of the ICP method have been proposed. In photogrammetry there have been some studies on the absolute orientation of stereo models using DEMs (Digital Elevation Model) as control information. These works are known as DEM matching, which corresponds mathematically with least squares image matching. The DEM matching concept is only applied to 2.5D surfaces. 2.5D surfaces have limited value, especially in close range applications. Our proposed method estimates the 3D similarity transformation parameters between two or more fully 3D surface patches, minimizing the Euclidean distances between the surfaces by least squares. This formulation gives the opportunity of matching arbitrarily oriented 3D surface patches. An observation equation is written for each surface element on the template surface patch, i.e. for each sampled point. The geometric relationship between the conjugate surface patches is defined as a 7parameter 3D similarity transformation. The constant term of the adjustment is given by the observation vector whose elements are the Euclidean distances between the template and search surface elements. Since the functional model is nonlinear, the solution is iteratively approaching to a global minimum. The unknown transformation parameters are treated as stochastic quantities using
A Multiple View 3D Registration Algorithm with Statistical Error Modeling
, 2000
"... this paper we focus upon the motion estimation task. AnearE applicationfrp the photogrA([]RE community is theprA[8# ofdeterRA([8 absoluteoro entation ofster# imager . Fixed,sur eyedgr887 contrn ..."
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Cited by 6 (0 self)
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this paper we focus upon the motion estimation task. AnearE applicationfrp the photogrA([]RE community is theprA[8# ofdeterRA([8 absoluteoro entation ofster# imager . Fixed,sur eyedgr887 contrn