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Integrating Surface Normal Vectors Using Fast Marching Method
| Book Series | Lecture Notes in Computer Science |
| Publisher | Springer Berlin / Heidelberg |
| ISSN | 0302-9743 (Print) 1611-3349 (Online) |
| Volume | Volume 3953/2006 |
| Book | Computer Vision – ECCV 2006 |
| DOI | 10.1007/11744078 |
| Copyright | 2006 |
| ISBN | 978-3-540-33836-9 |
| Category | Multiview Geometry and 3D Methods |
| DOI | 10.1007/11744078_19 |
| Pages | 239-250 |
| Subject Collection | Computer Science |
| SpringerLink Date | Wednesday, July 26, 2006 |
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Multiview Geometry and 3D Methods
Integrating Surface Normal Vectors Using Fast Marching Method
Jeffrey Ho1 , Jongwoo Lim2 , Ming-Hsuan Yang2 and David Kriegman3 
| (1) |
Department of CISE, University of Florida, Gainesville, FL, USA |
| (2) |
Honda Research Institute, Mountain View, CA, USA |
| (3) |
Department of Computer Science and Engineering, University of California, San Diego, CA, USA |
Abstract
Integration of surface normal vectors is a vital component in many shape reconstruction algorithms that require integrating
surface normals to produce their final outputs, the depth values. In this paper, we introduce a fast and efficient method
for computing the depth values from surface normal vectors. The method is based on solving the Eikonal equation using Fast
Marching Method. We introduce two ideas. First, while it is not possible to solve for the depths Z directly using Fast Marching Method, we solve the Eikonal equation for a function W of the form W = Z + λ f. With appropriately chosen values for λ, we can ensure that the Eikonal equation for W can be solved using Fast Marching Method. Second, we solve for W in two stages with two different λ values, first in a small neighborhood of the given initial point with large λ, and then for the rest of the domain with a smaller λ. This step is needed because of the finite machine precision and rounding-off errors. The proposed method is very easy to
implement, and we demonstrate experimentally that, with insignificant loss in precision, our method is considerably faster
than the usual optimization method that uses conjugate gradient to minimize an error function.
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