@inproceedings{KleefeldJuTozzaetal., author = {Kleefeld, Andreas and Ju, Yong Chul and Tozza, Silvia and Breuß, Michael and Bruhn, Andr{\´e}s}, title = {Generalised Perspective Shape from Shading with Oren-Nayar Reflectance}, series = {Proceedings 24th British Machine Vision Conference, BMVC 2013, Bristol, UK, September 2013}, booktitle = {Proceedings 24th British Machine Vision Conference, BMVC 2013, Bristol, UK, September 2013}, editor = {Burghardt, Tilo and Damen, Dima and Mayol-Cuevas, Walterio and Mirmehdi, Majid}, publisher = {BMVA Press}, doi = {10.5244/C.27.42}, pages = {42.1 -- 42.11}, abstract = {In spite of significant advances in Shape from Shading (SfS) over the last years, it is still a challenging task to design SfS approaches that are flexible enough to handle a wide range of input scenes. In this paper, we address this lack of flexibility by proposing a novel model that extends the range of possible applications. To this end, we consider the class of modern perspective SfS models formulated via partial differential equations (PDEs). By combining a recent spherical surface parametrisation with the advanced non-Lambertian Oren-Nayar reflectance model, we obtain a robust approach that allows to deal with an arbitrary position of the light source while being able to handle rough surfaces and thus more realistic objects at the same time. To our knowledge, the resulting model is currently the most advanced and most flexible approach in the literature on PDE-based perspective SfS. Apart from deriving our model, we also show how the corresponding set of sophisticated Hamilton-Jacobi equations can be efficiently solved by a specifically tailored fast marching scheme. Experiments with medical real-world data demonstrate that our model works in practice and that is offers the desired flexibility.}, language = {en} } @inproceedings{JuBreussBruhnetal., author = {Ju, Yong Chul and Breuß, Michael and Bruhn, Andr{\´e}s and Galiani, Silvano}, title = {Shape from Shading for Rough Surfaces: Analysis of the Oren-Nayar Model}, series = {Proceedings 23rd British Machine Vision Conference, BMVC 2012, Surrey, UK, September 3-7, 2012}, booktitle = {Proceedings 23rd British Machine Vision Conference, BMVC 2012, Surrey, UK, September 3-7, 2012}, editor = {Browden, R. and Collomosse, J. and Mikolajczyk, K.}, publisher = {BMVA Press}, isbn = {1-901725-46-4}, doi = {10.5244/C.26.104}, pages = {104.1 -- 104.11}, language = {en} } @inproceedings{GallianiJuBreussetal., author = {Galliani, S. and Ju, Yong Chul and Breuß, Michael and Bruhn, Andr{\´e}s}, title = {Generalised Perspective Shape from Shading in Spherical Coordinates}, series = {Proceedings Fourth International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2013, Graz, Austria, June 2013}, booktitle = {Proceedings Fourth International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2013, Graz, Austria, June 2013}, editor = {Kuijper, A. and Pock, T. and Bredies, K. and Bischof, H.}, publisher = {Springer}, address = {Berlin}, isbn = {978-3-642-38266-6}, pages = {222 -- 233}, language = {en} } @inproceedings{ChulJuBruhnBreuss, author = {Chul Ju, Yong and Bruhn, Andr{\´e}s and Breuß, Michael}, title = {Variational Perspective Shape from Shading}, series = {Scale Space and Variational Methods in Computer Vision, 5th International Conference, SSVM 2015, L{\`e}ge-Cap Ferret, France, May 31 - June 4, 2015, Proceedings}, booktitle = {Scale Space and Variational Methods in Computer Vision, 5th International Conference, SSVM 2015, L{\`e}ge-Cap Ferret, France, May 31 - June 4, 2015, Proceedings}, editor = {Aujol, Jean-Fran{\c{c}}ois and Nikolova, Mila and Papadakis, Nicolas}, publisher = {Springer International Publishing}, address = {Berlin}, isbn = {978-3-319-18460-9}, doi = {10.1007/978-3-319-18461-6_43}, pages = {538 -- 550}, language = {en} } @incollection{JuMaurerBreussetal., author = {Ju, Yong Chul and Maurer, Daniel and Breuß, Michael and Bruhn, Andr{\´e}s}, title = {Direct variational perspective shape from shading with Cartesian depth parametrisation}, series = {Perspectives in Shape Analysis, Mathematics and Visualization}, booktitle = {Perspectives in Shape Analysis, Mathematics and Visualization}, editor = {Breuß, Michael and Bruckstein, Alfred and Maragos, Petros and Wuhrer, Stefanie}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-24724-3}, doi = {10.1007/978-3-319-24726-7}, pages = {43 -- 72}, language = {en} } @misc{MaurerJuBreussetal., author = {Maurer, Daniel and Ju, Yong Chul and Breuß, Michael and Bruhn, Andr{\´e}s}, title = {Combining Shape from Shading and Stereo: A Variational Approach for the Joint Estimation of Depth, Illumination and Albedo}, publisher = {BMVA Press}, address = {York, UK}, abstract = {Shape from shading (SfS) and stereo are two fundamentally different strategies for image-based 3-D reconstruction. While approaches for SfS infer the depth solely from pixel intensities, methods for stereo are based on a matching process that establishes correspondences across images. In this paper we propose a joint variational method that combines the advantages of both strategies. By integrating recent stereo and SfS models into a single minimisation framework, we obtain an approach that exploits shading information to improve upon the reconstruction quality of robust stereo methods. To this end, we fuse a Lambertian SfS approach with a robust stereo model and supplement the resulting energy functional with a detail-preserving anisotropic second-order smoothness term. Moreover, we extend the novel model in such a way that it jointly estimates depth, albedo and illumination. This in turn makes it applicable to objects with non-uniform albedo as well as to scenes with unknown illumination. Experiments for synthetic and real-world images show the advantages of our combined approach: While the stereo part overcomes the albedo-depth ambiguity inherent to all SfS methods, the SfS part improves the degree of details of the reconstruction compared to pure stereo methods.}, language = {en} } @misc{MaurerJuBreussetal., author = {Maurer, Daniel and Ju, Yong Chul and Breuß, Michael and Bruhn, Andr{\´e}s}, title = {Combining Shape from Shading and Stereo: A Joint Variational Method for Estimating Depth, Illumination and Albedo}, series = {International Journal of Computer Vision}, volume = {126}, journal = {International Journal of Computer Vision}, number = {12}, issn = {0920-5691}, doi = {10.1007/s11263-018-1079-1}, pages = {1342 -- 1366}, abstract = {Shape from shading (SfS) and stereo are two fundamentally different strategies for image-based 3-D reconstruction. While approaches for SfS infer the depth solely from pixel intensities, methods for stereo are based on a matching process that establishes correspondences across images. This difference in approaching the reconstruction problem yields complementary advantages that are worthwhile being combined. So far, however, most "joint" approaches are based on an initial stereo mesh that is subsequently refined using shading information. In this paper we follow a completely different approach. We propose a joint variational method that combines both cues within a single minimisation framework. To this end, we fuse a Lambertian SfS approach with a robust stereo model and supplement the resulting energy functional with a detail-preserving anisotropic second-order smoothness term. Moreover, we extend the resulting model in such a way that it jointly estimates depth, albedo and illumination. This in turn makes the approach applicable to objects with non-uniform albedo as well as to scenes with unknown illumination. Experiments for synthetic and real-world images demonstrate the benefits of our combined approach: They not only show that our method is capable of generating very detailed reconstructions, but also that joint approaches are feasible in practice.}, language = {en} }