A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Flattening Curved Documents in Images
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Combining Cues: Shape from Shading and Texture
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
An Effective Approach to 3D Deformable Surface Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Semidefinite Programming Heuristics for Surface Reconstruction Ambiguities
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Laplacian meshes for monocular 3d shape recovery
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Spatiotemporal descriptor for wide-baseline stereo reconstruction of non-rigid and ambiguous scenes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Reconstructing 3d human pose from 2d image landmarks
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
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Recovering the 3D shape of deformable surfaces from single images is difficult because many different shapes 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 efficient approach to exploring the set of solutions of an objective function based on point-correspondences and to proposing a small set of candidate 3D shapes. This allows the use of 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.