By Topic

Linear Local Models for Monocular Reconstruction of Deformable Surfaces

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Mathieu Salzmann ; Toyota Technological Institute at Chicago, Chicago ; Pascal Fua

Recovering the 3D shape of a nonrigid surface from a single viewpoint is known to be both ambiguous and challenging. Resolving the ambiguities typically requires prior knowledge about the most likely deformations that the surface may undergo. It often takes the form of a global deformation model that can be learned from training data. While effective, this approach suffers from the fact that a new model must be learned for each new surface, which means acquiring new training data, and may be impractical. In this paper, we replace the global models by linear local models for surface patches, which can be assembled to represent arbitrary surface shapes as long as they are made of the same material. Not only do they eliminate the need to retrain the model for different surface shapes, they also let us formulate 3D shape reconstruction from correspondences as either an algebraic problem that can be solved in closed form or a convex optimization problem whose solution can be found using standard numerical packages. We present quantitative results on synthetic data, as well as qualitative results on real images.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:33 ,  Issue: 5 )