Anisotropic diffusion of surface normals for feature preserving surface reconstruction

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Publication Type Journal Article
School or College College of Engineering
Department Electrical & Computer Engineering
Creator Tasdizen, Tolga; Whitaker, Ross T.
Title Anisotropic diffusion of surface normals for feature preserving surface reconstruction
Date 2003
Description For 3D surface reconstruction problems with noisy and incomplete range data measured from complex scenes with arbitrary topologies, a low-level representation, such as level set surfaces, is used. Such surface reconstruction is typically accomplished by minimizing a weighted sum of data-model discrepancy and model smoothness terms. This paper introduces a new onlinear model smoothness term for surface reconstruction based on variations of the surface normals. A direct solution requires solving a fourth-order partial differential equation (PDE), which is very difficult with conventional numerical techniques. Our solution is based on processing the normals separately from the surface, which allows us to separate the problem into two second-order PDEs. The proposed method can smooth complex, noisy surfaces, while preserving sharp, geometric features, and it is a natural generalization of edge-preserving methods in image processing, such as anisotropic diffusion.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
First Page 70
Last Page 77
Language eng
Bibliographic Citation Tasdizen, T., & Whitaker, R. T. (2003). Anisotropic diffusion of surface normals for feature preserving surface reconstruction. Proceedings of 4th International Conference on 3D Digital Imaging and Modeling, 70-7. October.
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Rights Management (c) 2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Format Medium application/pdf
Format Extent 1,067,299 bytes
Identifier ir-main,15231
ARK ark:/87278/s6w09qfh
Setname ir_uspace
Date Created 2012-06-13
Date Modified 2021-05-06
ID 705745
Reference URL https://collections.lib.utah.edu/ark:/87278/s6w09qfh
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