Publication
Stochastic exploration of ambiguities for nonrigid shape recovery
Journal Article (2013)
Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pages
463-475
Volume
35
Number
2
Doc link
http://dx.doi.org/10.1109/TPAMI.2012.102
File
Abstract
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 a stochastic sampling approach to efficiently explore the set of solutions of an objective function based on point correspondences. This allows us 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 nonlinear minimization problem. The advantages of our method are demonstrated on a variety of problems including both real and synthetic data.
Categories
computer vision.
Author keywords
deformable surfaces, monocular shape estimation
Scientific reference
F. Moreno-Noguer and P. Fua. Stochastic exploration of ambiguities for nonrigid shape recovery. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(2): 463-475, 2013.
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