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Modeling range images with bounded error triangular meshes without optimization

Presents a technique for approximating range images by means of adaptive triangular meshes with a bounded approximation error and without applying optimization. This approach consists of three stages. In the first stage, every pixel of the given range image is mapped to a 3D point defined in a refer...

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Main Authors: Sappa, A.D., Garcia, M.A.
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Language:English
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Garcia, M.A.
description Presents a technique for approximating range images by means of adaptive triangular meshes with a bounded approximation error and without applying optimization. This approach consists of three stages. In the first stage, every pixel of the given range image is mapped to a 3D point defined in a reference frame associated with the range sensor. Then, those 3D points are mapped to a 3D curvature space. In the second stage, the points contained in this curvature space are triangulated through a 3D Delaunay algorithm, giving rise to a tetrahedronization of them. In the last stage, an iterative process starts digging the external surface of the previous tetrahedronization, removing those triangles that do not fulfill the given approximation error. In this way, successive fronts of triangular meshes are obtained in both range image space and curvature space. This iterative process is applied until a triangular mesh in the range image space fulfilling the given approximation error is obtained. Experimental results are presented.
doi_str_mv 10.1109/ICPR.2000.905360
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2831-7475
language eng
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Acceleration
Application software
Approximation error
Computer errors
Computer science
Computer vision
Image sensors
Mesh generation
Optimization methods
Pixel
title Modeling range images with bounded error triangular meshes without optimization
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