Building 3D models from unregistered multiple range images

Higuchi, Kazunori; Hebert, Martial; Ikeuchi, Katsushi

doi:10.1002/(sici)1520-684x(19980615)29:6<82::aid-scj8>3.0.co;2-h

Cited by 8 publications

(8 citation statements)

References 14 publications

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“…Another limitation of bitangent curves is that they represent global features, which may not be contained fully inside the overlapping region of the views. The Spherical Attribute Imaging (SAI), proposed by Higuchi et al [25], requires the underlying 3D surfaces to be free of topological holes, which limits its applicability. The feature matching technique proposed by Roth [26] is limited by the fact that it significantly relies upon the amount of texture on the object surface for consistent extraction of features.…”

Section: Related Workmentioning

confidence: 99%

A novel 3D vorticity based approach for automatic registration of low resolution range images

Shah

Bennamoun

Boussaid

2015

Pattern Recognition

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Section: Related Workmentioning

confidence: 99%

A novel 3D vorticity based approach for automatic registration of low resolution range images

Shah

Bennamoun

Boussaid

2015

Pattern Recognition

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“…Finally, the graph algorithm registers each individual scan with respect to a central pivot scan. There can be found many other feature-based ICP approaches in the literature (Higuchi et al, 1995;Chua and Jarvis, 1996;Feldmar and Ayache, 1996;Thirion, 1996;Soucy and Ferrie, 1997;Yang and Allen, 1998;Vanden Wyngaerd et al, 1999).…”

Section: Review Of Previous Work On Surface Matchingmentioning

confidence: 99%

Least squares 3D surface and curve matching

Gruen

Akça

2005

ISPRS Journal of Photogrammetry and Remote Sensing

484

276

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The automatic co-registration 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 Gauss-Markoff 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 close-range 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. D

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“…Again, in these techniques, the basic assumption is that the sets of points are dense. There are two types of reconstruction from range images: reconstructing parametric surfaces [6,7] or reconstructing an implicit function, usually a signed or weighted signed distance function [8][9][10]. These techniques deal with additional issues, such as the registration between different range images.…”

Section: Previous Workmentioning

confidence: 99%

Shape recovery and viewpoint planning for reverse engineering

Zetu

Akgündüz

2005

Int J Adv Manuf Technol

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In this paper, a new methodology for surface reconstruction from sets of points for reverse engineering is described. The sets of points are obtained using stereovision techniques. Techniques for minimizing the number of images required for a complete object reconstruction are explored based on characterizing the shapes to be recovered in terms of visibility and number and nature of cavities. The ultimate goal of our viewpoint planning technique is to minimize the number of images when no prior information about the objects is available.

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Building 3D models from unregistered multiple range images

Cited by 8 publications

References 14 publications

A novel 3D vorticity based approach for automatic registration of low resolution range images

A novel 3D vorticity based approach for automatic registration of low resolution range images

Least squares 3D surface and curve matching

Shape recovery and viewpoint planning for reverse engineering

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