Robust Surface Reconstruction
Event details
| Date | 27.05.2015 |
| Hour | 14:00 |
| Speaker | Dr Virginia Estellers, UCLA |
| Location | |
| Category | Conferences - Seminars |
We propose a method to reconstruct surfaces from oriented point clouds with non-uniform sampling and noise by formulating the problem as a convex minimization that reconstructs the indicator function of the surface’s interior. Compared to previous models, our reconstruction is robust to noise and outliers because it substitutes the least-squares fidelity term by a robust Huber penalty; this allows to recover sharp corners and avoids the shrinking bias of least squares. We choose an implicit parametrization to reconstruct surfaces of unknown topology and close large gaps in the point cloud.
For an efficient representation, we approximate the implicit function by a hierarchy of locally supported basis elements adapted to the geometry of the surface. Unlike ad-hoc bases over an octree, our hierarchical B-splines from isogeometric analysis locally adapt the mesh and degree of the splines during reconstruction. The hierarchical structure of the basis speeds-up the minimization and efficiently represents clustered data. We also advocate for convex optimization, instead isogeometric finite-element techniques, to efficiently solve the minimization and allow for non-differentiable functionals. Experiments show state-of-the-art performance within a more flexible framework. This is joint work with Michael Scott and Stefano Soatto.
Bio: Virginia Estellers received her PhD in image processing from Ecole Polythechnique Federale de Lausanne in 2013, and joined the UCLA Vision Lab as a postdoctoral fellow with an SNSF fellowship. Previous to that, she did her undergraduate studies at the Polytechnic University of Catalonia in both Mathematics and Electrical Engineering
Her research interests are in mathematical modeling and computational techniques in imaging and vision, particularly using variational methods, convex optimization, and partial differential equations. She is interested in the theoretical and physical aspects of the acquisition of images, their mathematical representations, and the development of efficient algorithms to extract information from them.
For an efficient representation, we approximate the implicit function by a hierarchy of locally supported basis elements adapted to the geometry of the surface. Unlike ad-hoc bases over an octree, our hierarchical B-splines from isogeometric analysis locally adapt the mesh and degree of the splines during reconstruction. The hierarchical structure of the basis speeds-up the minimization and efficiently represents clustered data. We also advocate for convex optimization, instead isogeometric finite-element techniques, to efficiently solve the minimization and allow for non-differentiable functionals. Experiments show state-of-the-art performance within a more flexible framework. This is joint work with Michael Scott and Stefano Soatto.
Bio: Virginia Estellers received her PhD in image processing from Ecole Polythechnique Federale de Lausanne in 2013, and joined the UCLA Vision Lab as a postdoctoral fellow with an SNSF fellowship. Previous to that, she did her undergraduate studies at the Polytechnic University of Catalonia in both Mathematics and Electrical Engineering
Her research interests are in mathematical modeling and computational techniques in imaging and vision, particularly using variational methods, convex optimization, and partial differential equations. She is interested in the theoretical and physical aspects of the acquisition of images, their mathematical representations, and the development of efficient algorithms to extract information from them.
Links
Practical information
- General public
- Free
Organizer
- Signal Processing Lab (LTS5)
Contact
- Thiran Jean-Philippe [[email protected]]