@inproceedings{BreussQueauBaehretal., author = {Breuß, Michael and Qu{\`e}au, Yvain and B{\"a}hr, Martin and Durou, Jean-Denis}, title = {Highly Efficient Surface Normal Integration}, series = {Proceedings of the Conference Algoritmy 2016, 20th Conference on Scientific Computing, Vysok{\´e} Tatry - Podbansk{\´e}, Slovakia, March 14 - 18}, booktitle = {Proceedings of the Conference Algoritmy 2016, 20th Conference on Scientific Computing, Vysok{\´e} Tatry - Podbansk{\´e}, Slovakia, March 14 - 18}, publisher = {Publishing House of Slovak University of Technology in Bratislava}, address = {Bratislava}, isbn = {978-80-227-4544-4}, pages = {204 -- 213}, language = {en} } @misc{HoeltgenQueauBreussetal., author = {Hoeltgen, Laurent and Queau, Yvain and Breuß, Michael and Radow, Georg}, title = {Optimised photometric stereo via non-convex variational minimisation}, publisher = {BMVA Press}, address = {York, UK}, pages = {1}, language = {en} } @misc{BaehrBreussQueauetal., author = {B{\"a}hr, Martin and Breuß, Michael and Qu{\`e}au, Yvain and Sharifi Boroujerdi, Ali and Durou, Jean-Denis}, title = {Fast and accurate surface normal integration on non-rectangular domains}, series = {Computational Visual Media}, volume = {3}, journal = {Computational Visual Media}, number = {2}, issn = {2096-0433}, doi = {10.1007/s41095-016-0075-z}, pages = {107 -- 129}, 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.}, language = {en} } @misc{RadowHoeltgenQueauetal., author = {Radow, Georg and Hoeltgen, Laurent and Qu{\´e}au, Yvain and Breuß, Michael}, title = {Optimisation of Classic Photometric Stereo by Non-convex Variational Minimisation}, series = {Journal of Mathematical Imaging and Vision}, volume = {61}, journal = {Journal of Mathematical Imaging and Vision}, number = {1}, issn = {1573-7683}, doi = {10.1007/s10851-018-0828-7}, pages = {84 -- 105}, 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, photometric stereo is distinguished by the assumption that the underlying input images are taken from the same point of view but under different lighting conditions. The most common techniques are conceptually close to the classic photometric stereo problem, meaning that the modelling encompasses a linearisation step and that the shape information is computed in terms of surface normals. In this work, instead of linearising we aim to stick to the original formulation of the photometric stereo problem, and we 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 photometric stereo 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 other approaches that rely on the classic photometric stereo assumptions. The experiments also highlight some practical aspects of the underlying optimisation algorithm that may be of interest in a more general context.}, language = {en} }