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Geometrically Stable Sampling for the ICP Algorithm
 Proc. International Conference on 3D Digital Imaging and Modeling
, 2003
"... The Iterative Closest Point (ICP) algorithm is a widely used method for aligning threedimensional point sets. The quality of alignment obtained by this algorithm depends heavily on choosing good pairs of corresponding points in the two datasets. If too many points are chosen from featureless region ..."
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Cited by 65 (5 self)
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The Iterative Closest Point (ICP) algorithm is a widely used method for aligning threedimensional point sets. The quality of alignment obtained by this algorithm depends heavily on choosing good pairs of corresponding points in the two datasets. If too many points are chosen from featureless regions of the data, the algorithm converges slowly, finds the wrong pose, or even diverges, especially in the presence of noise or miscalibration in the input data. In this paper, we describe a method for detecting uncertainty in pose, and we propose a point selection strategy for ICP that minimizes this uncertainty by choosing samples that constrain potentially unstable transformations.
Matching of 3D surfaces and their intensities
, 2007
"... 3D surface matching would be an ill conditioned problem when the curvature of the object surface is either homogenous or isotropic, e.g. for plane or spherical types of objects. A reliable solution can only be achieved if supplementary information or functional constraints are introduced. In a previ ..."
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Cited by 17 (2 self)
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3D surface matching would be an ill conditioned problem when the curvature of the object surface is either homogenous or isotropic, e.g. for plane or spherical types of objects. A reliable solution can only be achieved if supplementary information or functional constraints are introduced. In a previous paper, an algorithm for the least squares matching of overlapping 3D surfaces, which were digitized/sampled point by point using a laser scanner device, by the photogrammetric method or other techniques, was proposed [Gruen, A., and Akca, D., 2005. Least squares 3D surface and curve matching. ISPRS Journal of Photogrammetry and Remote Sensing 59 (3), 151–174.]. That method estimates the transformation parameters between two or more fully 3D surfaces, minimizing the Euclidean distances instead of zdifferences between the surfaces by least squares. In this paper, an extension to the basic algorithm is given, which can simultaneously match surface geometry and its attribute information, e.g. intensity, colour, temperature, etc. under a combined estimation model. Three experimental results based on terrestrial laser scanner point clouds are presented. The experiments show that the basic algorithm can solve the surface matching problem provided that the object surface has at least the minimal information. If not, the laser scanner derived intensities are used as supplementary information to find a reliable solution. The method derives its mathematical strength from the least squares image matching concept and offers a high level of flexibility for many kinds of 3D surface correspondence problem.
Fast correspondence search for 3D surface matching
 International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36 (Part 3/W19
, 2005
"... An algorithm for least squares matching of overlapping 3D surfaces is presented. It estimates the transformation parameters between two or more fully 3D surfaces, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between the surfaces. This formulatio ..."
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Cited by 12 (7 self)
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An algorithm for least squares matching of overlapping 3D surfaces is presented. It estimates the transformation parameters between two or more fully 3D surfaces, 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 surfaces simultaneously, without using explicit tie points. Besides the mathematical model and execution aspects we pay particular interest to the reduction of the computational expenses. An efficient space partitioning method is implemented in order to speed up the correspondence search, which is the main portion of the computational efforts. The simultaneous matching of subsurface patches is given as another strategy. It provides a computationally effective solution, since it matches only relevant multisubpatches rather then the whole overlapping area. A practical example including computation times is given for the demonstration of the method. 1.
Recent advances in least squares 3D surface matching
 OPTICAL 3D MEASUREMENT TECHNIQUES VII
, 2005
"... We present an algorithm for the least squares matching of overlapping 3D surfaces. It estimates the transformation parameters between two or more fully 3D surfaces, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between the surfaces. This formula ..."
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Cited by 3 (1 self)
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We present an algorithm for the least squares matching of overlapping 3D surfaces. It estimates the transformation parameters between two or more fully 3D surfaces, 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 surfaces simultaneously, without using explicit tie points. Besides the mathematical model of the procedure, we discuss the computational aspects. We give practical examples to demonstrate the method.
Practical nonlinear photometric projector compensation
 in Computer Vision and Pattern Recognition Workshops (CVPRW), 2013 IEEE Conference on. IEEE, 2013
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LEAST SQUARES MATCHING OF 3D SURFACES
"... An algorithm for the least squares matching of overlapping 3D surfaces is presented. It estimates the transformation parameters of one or more fully 3D surfaces with respect to a template one, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between ..."
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An algorithm for the least squares matching of overlapping 3D surfaces is presented. It estimates the transformation parameters of one or more fully 3D surfaces with respect to a template one, 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 surfaces simultaneously, without using explicit tie points. Besides the mathematical model of the procedure, we discuss the computational aspects. We give practical examples to demonstrate the method.
Automatic 3D Registration using Range and Colour Data
, 2004
"... Automatic 3D Registration using Range and Colour Data refers to the process of automatically joining a pair of overlapping colour 3D images acquired using a laser range finder. Currently there are no methods that can automatically and reliably register overlapping 3D images. This Thesis presents a s ..."
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Automatic 3D Registration using Range and Colour Data refers to the process of automatically joining a pair of overlapping colour 3D images acquired using a laser range finder. Currently there are no methods that can automatically and reliably register overlapping 3D images. This Thesis presents a solution to the problem of automatic registration, which uses both the 3D data, along with coregistered 2D colour information. Features are found in the 2D data, but both 2D and 3D descriptors are attached to these feature points. The correspondences between these feature points in the two overlapping images are determined, and the transformation between the overlapping range images is computed from the best correspondences. The combination of 2D and 3D data exploits both the object geometry, as well as the object colour. Both the feature finding and correspondence process are automatic, and do not require any user intervention. A number of experiments are conducted to demonstrate the effectiveness of the approach.
Statistical Error Model of Active Triangulation Method for CAI
"... We develop a statistical error model for reliable computeraided inspection (CAI) of industrial parts with the use of active triangulation optical scanning system. The work devoted to statistical analysis and accuracy evaluation of algorithms of the method. We start from obtaining an anisotropic err ..."
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We develop a statistical error model for reliable computeraided inspection (CAI) of industrial parts with the use of active triangulation optical scanning system. The work devoted to statistical analysis and accuracy evaluation of algorithms of the method. We start from obtaining an anisotropic error field for triangulation and use it as a base for further algorithms analysis. Then we construct statistically optimal algorithms using the error model of input data. We show the evolution of error through all stages of the processing pipeline. The error model of the method allows us to estimate the achieved accuracy. The use of error modeling for tolerance control makes the results more reliable and allows giving directions how to improve the accuracy of the measurements.