Sciweavers

Share
CVPR
2009
IEEE

Continuous Ratio Optimization via Convex Relaxation with Applications to Multiview 3D Reconstruction

11 years 11 months ago
Continuous Ratio Optimization via Convex Relaxation with Applications to Multiview 3D Reconstruction
We introduce a convex relaxation framework to optimally minimize continuous surface ratios. The key idea is to minimize the continuous surface ratio by solving a sequence of convex optimization problems. We show that such minimal ratios are superior to traditionally used minimal surface formulations in that they do not suffer from a shrinking bias and no longer require the choice of a regularity parameter. The absence of a shrinking bias in the minimal ratio model is proven analytically. Furthermore we demonstrate that continuous ratio optimization can be applied to derive a new algorithm for reconstructing three-dimensional silhouette-consistent objects from multiple views. Experimental results confirm that our approach allows to accurately reconstruct deep concavities even without the specification of tuning parameters.
Kalin Kolev (University of Bonn), Daniel Cremers (
Added 09 May 2009
Updated 10 Dec 2009
Type Conference
Year 2009
Where CVPR
Authors Kalin Kolev (University of Bonn), Daniel Cremers (University of Bonn)
Comments (0)
books