Ruttle, Jonathan and Manzke, Michael and Dahyot, Rozenn
(2010)
Smooth Kernel Density Estimate for Multiple View Reconstruction.
proceedings of The 7th European Conference for Visual Media Production, CVMP 2010.
Abstract
We present a statistical framework to merge the information
from silhouettes segmented in multiple view images to infer
the 3D shape of an object. The approach is generalising the
robust but discrete modelling of the visual hull by using the
concept of averaged likelihoods. One resulting advantage of
our framework is that the objective function is continuous and
therefore an iterative gradient ascent algorithm can be defined
to efficiently search the space. Moreover this results in a
method which is less memory demanding and one that is very
suitable to a parallel processing architecture. Experimental
results shows that this approach is efficient for getting a
robust initial guess to the 3D shape of an object in view.
| Item Type: |
Article
|
| Keywords: |
Shape from silhouette; Kernel Density estimate;
Newton-Raphson; |
| Academic Unit: |
Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: |
15282 |
| Identification Number: |
https://doi.org/10.1109/CVMP.2010.17 |
| Depositing User: |
Rozenn Dahyot
|
| Date Deposited: |
19 Jan 2022 12:24 |
| Journal or Publication Title: |
proceedings of The 7th European Conference for Visual Media Production, CVMP 2010 |
| Refereed: |
Yes |
| URI: |
|
| Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
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