Name
Abstract | ||
|---|---|---|
Estimating shape and appearance of a three dimensional object from a given set of images is a classic research topic that is still actively pursued. Among the various techniques available, PS is distinguished by the assumption that the underlying input images are taken from the same point of view but under different lighting conditions. most common techniques provide the shape information in terms of surface normals. In this work, we instead propose to minimise a much more natural objective function, namely the reprojection error in terms of depth. Minimising the resulting non-trivial variational model for PS allows to recover the depth of the photographed scene directly. As a solving strategy, we follow an approach based on a recently published optimisation scheme for non-convex and non-smooth cost functions. The main contributions of our paper are of theoretical nature. A technical novelty in our framework is the usage of matrix differential calculus. We supplement our approach by a detailed convergence analysis of the resulting optimisation algorithm and discuss possibilities to ease the computational complexity. At hand of an experimental evaluation we discuss important properties of the method. Overall, our strategy achieves more accurate results than competing approaches. experiments also highlights some practical aspects of the underlying optimisation algorithm that may be of interest in a more general context. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Convergence (routing),Reprojection error,Mathematical optimization,Matrix (mathematics),Computer science,Regular polygon,Differential calculus,Minimisation (psychology),Artificial intelligence,Machine learning,Photometric stereo,Computational complexity theory |
DocType | Volume | Citations |
Journal | abs/1709.10437 | 0 |
PageRank | References | Authors |
0.34 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Georg Radow | 1 | 0 | 0.68 |
| Laurent Hoeltgen | 2 | 30 | 6.05 |
| Yvain Quéau | 3 | 76 | 12.83 |
| Michael Breuß | 4 | 168 | 25.45 |