@conference {vianello2018,
	title = {Robust Hough transform based 3D reconstruction from circular light fields},
	booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2018},
	doi = {10.1109/CVPR.2018.00765},
	author = {Alessandro Vianello and Jens Ackermann and Maximilian Diebold and Bernd J{\"a}hne}
}
@proceedings {6267,
	title = {Trust your Model: Light Field Depth Estimation with inline Occlusion Handling},
	year = {2018},
	doi = {10.1109/CVPR.2018.00476},
	author = {Hendrik Schilling and Maximilian Diebold and Carsten Rother and Bernd J{\"a}hne}
}
@conference {Schilling2018,
	title = {Trust your Model: Light Field Depth Estimation with Inline Occlusion Handling},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2018},
	pages = {4530{\textendash}4538},
	abstract = {We address the problem of depth estimation from light-field images. Our main contribution is a new way to handle occlusions which improves general accuracy and quality of object borders. In contrast to all prior work we work with a model which directly incorporates both depth and occlusion, using a local optimization scheme based on the PatchMatch algorithm. The key benefit of this joint approach is that we utilize all available data, and not erroneously discard valuable information in pre-processing steps. We see the benefit of our approach not only at improved object boundaries, but also at smooth surface reconstruction, where we outperform even methods which focus on good surface regularization. We have evaluated our method on a public light-field dataset, where we achieve state-of-the-art results in nine out of twelve error metrics, with a close tie for the remaining three.},
	isbn = {9781538664209},
	issn = {10636919},
	doi = {10.1109/CVPR.2018.00476},
	author = {Schilling, Hendrik and Maximilian Diebold and Carsten Rother and Bernd J{\"a}hne}
}
@article {Vianello-etal-2017-TM,
	title = {3D reconstruction by a combined structure tensor and Hough transform light field approach},
	journal = {tm - Technisches Messen},
	year = {2017},
	abstract = {Disparity estimation using the structure tensor is a local approach to determine orientation in Epipolar Plane Images. A global extension would lead to more precise and robust estimations. In this work, a novel algorithm for 3D reconstruction from linear light fields is proposed. This method uses a modified version of the Progressive Probabilistic Hough Transform to extract orientations from Epipolar Plane Images, allowing to achieve high quality disparity maps. To this aim, the structure tensor estimates are used to speed up computation and improve the disparity estimation near occlusion boundaries. The new algorithm is evaluated on both synthetic and real light field datasets, and compared with classical local disparity estimation techniques based on the structure tensor.},
	doi = {10.1515/teme-2017-0010},
	author = {Alessandro Vianello and Giulio Manfredi and Maximilian Diebold and Bernd J{\"a}hne}
}
@article {schilling2017,
	title = {On the design of a fractal calibration pattern for improved camera calibration},
	journal = {tm - Technisches Messen},
	volume = {84},
	year = {2017},
	pages = {440{\textendash}451},
	doi = {10.1515/teme-2017-0013},
	author = {Hendrik Schilling and Maximilian Diebold and Marcel Gutsche and Bernd J{\"a}hne}
}
@proceedings {schilling-etal-2016-ForumBildverarbeitung,
	title = {3D Reconstruction by a Combined Structure Tensor and Hough Transform Light-Field Approach},
	year = {2016},
	abstract = {Disparity estimation using the structure tensor is a local approach to determine orientation in Epipolar Plane Images. A global extension would lead to more precise and robust estimations. In this work, a novel algorithm for 3D reconstruction from linear light fields is porposed. It uses a modified Progressive Probabilistic Hough Transform, in combination with the structure tensor, to extract orientations from Epipolar Plane Images edge maps, allowing to achieve high quality disparity maps.},
	isbn = {978-3-7315-0587-7},
	doi = {10.5445/KSP/1000059899},
	url = {https://doi.org/10.5445/KSP/1000059899},
	author = {Alessandro Vianello and Giulio Manfredi and Maximilian Diebold and Bernd J{\"a}hne}
}
@proceedings {schilling-etal-2016-ForumBildverarbeitung,
	title = {A fractal calibration pattern for improved camera calibration},
	year = {2016},
	abstract = {Camera calibration, crucial for computer vision tasks, often relies on planar calibration targets to calibrate the camera parameters. This work explores a planar, fractal, self-identifying calibration pattern, which provides a high density of calibration points for a large range of magnification factors. An evaluation on ground truth data shows the target provides very high accuracy over a wide range of conditions.},
	doi = {10.5445/KSP/1000059899},
	url = {https://doi.org/10.5445/KSP/1000059899},
	author = {Hendrik Schilling and Maximilian Diebold and Marcel Gutsche and Hamza Aziz-Ahmad and Bernd J{\"a}hne}
}
@conference {DBLP:conf/cvpr/DieboldJG16,
	title = {Heterogeneous Light Fields},
	booktitle = {2016 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2016, Las Vegas, NV, USA, June 27-30, 2016},
	year = {2016},
	abstract = {In contrast to traditional binocular or multi-view stereo approaches, the adequately sampled space of observations in light-field imaging allows, to obtain dense and high quality depth maps. It also extends capabilities beyond those of traditional methods. Previously, constant intensity has been assumed for estimating disparity of orientation in most approaches to analyze epipolar plane images (EPIs). Here, we introduce a modified structure tensor approach which improves depth estimation. This extension also includes a model of non-constant intensity on EPI manifolds. We derive an approach to estimate high quality depth maps in luminance-gradient light fields, as well as in color-filtered light fields. Color-filtered light fields pose particular challenges due to the fact that structures can change significantly in appearance with wavelength and can completely vanish at some wavelength. We demonstrate solutions to this challenge and obtain a dense sRGB image reconstruction in addition to dense depth maps.},
	doi = {10.1109/CVPR.2016.193},
	url = {http://dx.doi.org/10.1109/CVPR.2016.193},
	author = {Maximilian Diebold and Alexander Gatto and Bernd J{\"a}hne}
}
@phdthesis {diebold2016,
	title = {Light-Field Imaging and Heterogeneous Light Fields},
	volume = {Dissertation},
	year = {2016},
	publisher = {IWR, Univ. Heidelberg},
	type = {phd},
	doi = {10.11588/heidok.00020560},
	author = {Maximilian Diebold}
}
@inbook {Krolla-etal-2015-LF4CV,
	title = {Light Field from Smartphone-Based Dual Video},
	booktitle = {Computer Vision - ECCV 2014 Workshops: Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part II},
	year = {2015},
	pages = {600{\textendash}610},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	isbn = {978-3-319-16181-5},
	doi = {10.1007/978-3-319-16181-5_46},
	url = {http://dx.doi.org/10.1007/978-3-319-16181-5_46},
	author = {Bernd Krolla and Maximilian Diebold and Didier Stricker}
}
@proceedings {diebold-etal-2015-SPIE,
	title = {Light-field camera design for high-accuracy depth estimation},
	year = {2015},
	month = {06/2015},
	abstract = {Light-field imaging is a research field with applicability in a variety of imaging areas including 3D cinema, entertainment, robotics, and any task requiring range estimation. In contrast to binocular or multi-view stereo approaches, capturing light fields means densely observing a target scene through a window of viewing directions. A principal benefit in light-field imaging for range computation is that one can eliminate the error-prone and computationally expensive process of establishing correspondence. The nearly continuous space of observation allows to compute highly accurate and dense depth maps free of matching. Here, we discuss how to structure the imaging system for optimal ranging over a defined volume - what we term a bounded frustum. We detail the process of designing the light-field setup, including practical issues such as camera footprint and component size influence the depth of field, lateral and range resolution. Both synthetic and real captured scenes are used to analyze the depth precision resulting from a design, and to show how unavoidable inaccuracies such as camera position and focal length variation limit depth precision. Finally, inaccuracies may be sufficiently well compensated through calibration and must be eliminated at the outset.},
	doi = {http://dx.doi.org/10.1117/12.2184845},
	author = {Maximilian Diebold and Oliver Blum and Marcel Gutsche and Sven Wanner and Christoph S. Garbe and Harlyn Baker and Bernd J{\"a}hne}
}
@conference {krolla-etall-2014-BMVC,
	title = {Spherical Light Fields},
	booktitle = {Proceedings of the British Machine Vision Conference},
	year = {2014},
	publisher = {BMVA Press},
	organization = {BMVA Press},
	abstract = {A full view spherical camera exploits its extended field of view to map the complete environment onto a 2D image plane. Thus, with a single shot, it delivers a lot more information about the surroundings than one can gather with a normal perspective or plenoptic camera, which are commonly used in light field imaging. However, in contrast to a light field camera, a spherical camera does not capture directional information about the incident light, and thus a single shot from a spherical camera is not sufficient to reconstruct 3D scene geometry. In this paper, we introduce a method combining spherical imaging with the light field approach. To obtain 3D information with a spherical camera, we capture several independent spherical images by applying a constant vertical offset between the camera positions and combine the images in a Spherical Light Field (SLF). We can then compute disparity maps by structure tensor orientation analysis on epipolar plane images, which in this context are 2D cuts through the spherical light field with constant azimuth angle. This method competes with the acquisition range of laser scanners and allows for a fast and extensive recording of a given scene. We benchmark our approach by comparing disparity maps of ray-traced scenes against its ground truth. Further we provide disparity maps of real world datasets. },
	author = {Bernd Krolla and Maximilian Diebold and Bastian Goldl{\"u}cke and Didier Stricker}
}
@conference {diebold-etal-2013-VMV,
	title = {Epipolar Plane Image Refocusing for Improved Depth Estimation and Occlusion Handling.},
	booktitle = {VMV},
	year = {2013},
	publisher = {Eurographics Association},
	organization = {Eurographics Association},
	abstract = {In contrast to traditional imaging, the higher dimensionality of a light field offers directional information about the
captured intensity. This information can be leveraged to estimate the disparity of 3D points in the captured scene.
A recent approach to estimate disparities analyzes the structure tensor and evaluates the orientation on epipolar
plane images (EPIs). While the resulting disparity maps are generally satisfying, the allowed disparity range is
small and occlusion boundaries can become smeared and noisy. In this paper, we first introduce an approach to
extend the total allowed disparity range. This allows for example the investigation of camera setups with a larger
baseline, like in the Middlebury 3D light fields. Second, we introduce a method to handle the difficulties arising at
boundaries between fore- and background objects to achieve sharper edge transitions.},
	keywords = {dblp},
	isbn = {978-3-905674-51-4},
	url = {http://dblp.uni-trier.de/db/conf/vmv/vmv2013.html$\#$DieboldG13},
	author = {Maximilian Diebold and Bastian Goldl{\"u}cke}
}
