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Adaptive reconstruction of freeform objects with 3D SOM neural network grids

Adaptive reconstruction of freeform objects with 3D SOM neur...

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作者： Barhak, J. Fischer, A. CMSR Laboratory for Computer Graphics and CAD Department of Mechanical Engineering Technion-Israel Institute of Technology Haifa Israel

There are several open problems that are viewed as a bottleneck in the reverse engineering process: (1) The topology is unknown;therefore, point connectivity relations are undefined. (2) The fitted surface must satisfy global and local shape preservation criteria, which are undefined explicitly. The reconstruction is based on parameterization and fitting stages. However, the above problems are influenced mainly by the parameterization. To overcome the above problems, the neural network self-organizing map (SOM) method is proposed for creating a 3D parametric grid. The main advantage of the SOM method is that it detects the orientation of the grid and the position of the sub-boundaries. Then through an adaptive process the neural network grid is converged to the sampled shape. The SOM method is applied directly on a 3D grid and avoids projection anomalies, which are common to other methods. For the surface fitting stage the random surface error correction fitting method, which is based on the SOM method, was developed and implemented. © 2002 Elsevier Science Ltd. All rights reserved.

关键词： Reverse engineering

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Adaptive parameterization for reconstruction of 3D freeform objects from laser-scanned data 7

Adaptive parameterization for reconstruction of 3D freeform ...

引用

7th Pacific Conference on computer graphics and Applications, Pacific graphics 1999

作者： Fischer, A. Manor, A. Barhak, Y. CMSR Laboratory for Computer Graphics and CAD Department of Mechanical Engineering Technion - Israel Institute of Technology Haifa32000 Israel

ISBN: (纸本)0769502938

This paper proposes a new adaptive parametrization method to overcome the problems of using laser scanners in Reverse Engineering. The method is based on the following stages: (a) Detecting a 3D boundary of the parametric base surface by matching an intensity image from a CCD camera with the scanned data. (b) Adaptively reconstructing a parametric base surface with a non-self intersecting grid, based on a PDE method (c) Calculating the parametrization of the 3D sampled points. (d) Fitting the surface by applying a least square (LSQ) approximation method with boundary constraints. The feasibility of the parametrization algorithm is demonstrated on several examples using sculptured free objects. © 1999 IEEE.

关键词： Reverse engineering

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Topology recognition of 3D closed freeform objects based on topological graphs

Topology recognition of 3D closed freeform objects based on ...

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Pacific Conference on computer graphics and Applications

作者： D. Steiner A. Fischer CMSR Laboratory for Computer Graphics and CAD Department of Mechanical Engineering Technion-Israel Institute of Technology Haifa Israel

ISBN: (纸本)0769512275

Reverse engineering (RE) deals with an enormous number of irregular and scattered digitized points that require intensive processing in order to reconstruct the surfaces of an object. Surface reconstruction of freeform objects is based on geometrical and topological criteria. Current fitting methods reconstruct an object using a bottom-up approach, from points to a dense mesh, and finally into smoothed connected freeform sub-surfaces. This type of reconstruction, however, can cause topological problems that lead to undesired surface fitting results. Such problems are particularly common with concave shapes. To avoid problems of this type, the paper proposes a new method that automatically detects the topological structure of an object as a base for surface fitting. The topological reconstruction method described in the paper is based on two stages: (1) creating 3D non-self-intersecting iso-curves from a 3D triangular mesh and (2) extracting a topological graph. The feasibility of the proposed topological reconstruction method is demonstrated on several examples using freeform objects with complex topologies.

关键词： Topology Surface fitting Surface reconstruction Shape Reconstruction algorithms Reverse engineering Laboratories computer graphics Mechanical engineering Scattering

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Adaptive reconstruction of freeform objects with 3D SOM neural network grids

Adaptive reconstruction of freeform objects with 3D SOM neur...

引用

Pacific Conference on computer graphics and Applications

作者： J. Barhak A. Fischer CMSR Laboratory for Computer Graphics and CAD Department of Mechanical Engineering Technion-Israel Institute of Technology Haifa Israel

Reverse engineering is an important process in cad systems today. Yet several open problems lead to a bottleneck in the reverse engineering process. First, because the topology of the object to be reconstructed is unknown, point connectivity relations are undefined. Second, the fitted surface must satisfy global and local shape preservation criteria that are undefined explicitly. In reverse engineering, object reconstruction is based both on parameterization and on fitting. Nevertheless, the above problems are influenced mainly by parameterization. In order to overcome the above problems, the paper proposes a neural network, Self Organizing Map (SOM) method, for creating a 3D parametric grid. The main advantage of the proposed SOM method is that it detects both the orientation of the grid and the position of the sub-boundaries. The neural network grid converges to the sampled shape through an adaptive learning process. The SOM method is applied directly on 3D sampled data and avoids the projection anomalies common to other methods. The paper also presents boundary correction and growing grid extensions to the SOM method. In the surface fitting stage, an RSEC (Random Surface Error Correction) fitting method based on the SOM method was developed and implemented.

关键词： Neural networks Surface fitting Reverse engineering Surface reconstruction Shape Spline Laser modes Data mining Jacobian matrices Laboratories

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A graphic view of linear algebra

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computer-Aided Design 2000年第4期32卷 295-295页

作者： A Fischer Technion—Israel Institute of Technology Faculty of Mechanical Engineering CMSR Laboratory for Computer Graphics and CAD Haifa Israel

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