Name
Abstract | ||
|---|---|---|
The integration of surface normals for the purpose of computing the shape of a surface in 3D space is a classic problem in computer vision. However, even nowadays it is still a challenging task to devise a method that is flexible enough to work on non-trivial computational domains with high accuracy, robustness, and computational efficiency. By uniting a classic approach for surface normal integration with modern computational techniques, we construct a solver that fulfils these requirements. Building upon the Poisson integration model, we use an iterative Krylov subspace solver as a core step in tackling the task. While such a method can be very efficient, it may only show its full potential when combined with suitable numerical preconditioning and problem-specific initialisation. We perform a thorough numerical study in order to identify an appropriate preconditioner for this purpose. To provide suitable initialisation, we compute this initial state using a recently developed fast marching integrator. Detailed numerical experiments illustrate the benefits of this novel combination. In addition, we show on real-world photometric stereo datasets that the developed numerical framework is flexible enough to tackle modern computer vision applications. |
Year | Venue | Keywords |
|---|---|---|
2017 | Computational Visual Media | surface normal integration, Poisson integration, conjugate gradient method, preconditioning, fast marching method, Krylov subspace methods, photometric stereo, 3D reconstruction |
DocType | Volume | Issue |
Journal | abs/1610.06049 | 2 |
Citations | PageRank | References |
5 | 0.40 | 21 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Bähr | 1 | 5 | 0.40 |
| Michael Breuß | 2 | 168 | 25.45 |
| Yvain Quéau | 3 | 76 | 12.83 |
| Ali Sharifi Boroujerdi | 4 | 12 | 2.25 |
| Jean-Denis Durou | 5 | 273 | 20.42 |