Anisotropic diffusion of surface normals for feature preserving surface reconstruction | IEEE Conference Publication | IEEE Xplore

Anisotropic diffusion of surface normals for feature preserving surface reconstruction


Abstract:

For 3D surface reconstruction problems with noisy and incomplete range data measured from complex scenes with arbitrary topologies, a low-level representation, such as le...Show More

Abstract:

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 datamodel discrepancy and model smoothness terms. We introduce a new nonlinear 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.
Date of Conference: 06-10 October 2003
Date Added to IEEE Xplore: 27 October 2003
Print ISBN:0-7695-1991-1
Conference Location: Banff, AB, Canada

References

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