Stochastic Exploration of Ambiguities for Non-Rigid Shape Recovery

Recovering the 3D shape of deformable surfaces from single images is known to be a highly ambiguous problem because many different shapes may have very similar projections. This is commonly addressed by restricting the set of possible shapes to linear combinations of deformation modes and by imposing additional geometric constraints. Unfortunately, because image measurements are noisy, such constraints do not always guarantee that the correct shape will be recovered. To overcome this limitation, we introduce an stochastic sampling approach to efficiently explore the set of solutions of an objective function based on point correspondences. This allows to propose a small set of ambiguous candidate 3D shapes and then use additional image information to choose the best one. As a proof of concept, we use either motion or shading cues to this end and show that we can handle a complex objective function without having to solve a difficult non-linear minimization problem. The advantages of our method are demonstrated on a variety of problems including both real and synthetic data.


Published in:
IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 2, 463-475
Year:
2013
Publisher:
Los Alamitos, Institute of Electrical and Electronics Engineers
ISSN:
0162-8828
Keywords:
Laboratories:




 Record created 2012-04-17, last modified 2018-09-13

Preprint:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)