@conference {Leistner2019,
	title = {Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift},
	booktitle = {Proceedings - 2019 International Conference on 3D Vision, 3DV 2019},
	year = {2019},
	month = {sep},
	pages = {249{\textendash}257},
	abstract = {We propose a method for depth estimation from light field data, based on a fully convolutional neural network architecture. Our goal is to design a pipeline which achieves highly accurate results for small-and wide-baseline light fields. Since light field training data is scarce, all learning-based approaches use a small receptive field and operate on small disparity ranges. In order to work with wide-baseline light fields, we introduce the idea of EPI-Shift: To virtually shift the light field stack which enables to retain a small receptive field, independent of the disparity range. In this way, our approach {\textquoteright}learns to think outside the box of the receptive field". Our network performs joint classification of integer disparities and regression of disparity-offsets. A U-Net component provides excellent long-range smoothing. EPI-Shift considerably outperforms the state-of-the-art learning-based approaches and is on par with hand-crafted methods. We demonstrate this on a publicly available, synthetic, small-baseline benchmark and on large-baseline real-world recordings.},
	keywords = {Computer vision, deep learning, depth estimation, light fields, Stereo},
	isbn = {9781728131313},
	doi = {10.1109/3DV.2019.00036},
	url = {http://arxiv.org/abs/1909.09059 http://dx.doi.org/10.1109/3DV.2019.00036},
	author = {Titus Leistner and Schilling, Hendrik and Mackowiak, Radek and Gumhold, Stefan and Carsten Rother}
}
@conference {Krull2015,
	title = {Learning analysis-by-synthesis for 6d pose estimation in RGB-D images},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2015 Inter},
	year = {2015},
	pages = {954{\textendash}962},
	abstract = {Analysis-by-synthesis has been a successful approach for many tasks in computer vision, such as 6D pose estimation of an object in an RGB-D image which is the topic of this work. The idea is to compare the observation with the output of a forward process, such as a rendered image of the object of interest in a particular pose. Due to occlusion or complicated sensor noise, it can be difficult to perform this comparison in a meaningful way. We propose an approach that "learns to compare", while taking these difficulties into account. This is done by describing the posterior density of a particular object pose with a convolutional neural network (CNN) that compares observed and rendered images. The network is trained with the maximum likelihood paradigm. We observe empirically that the CNN does not specialize to the geometry or appearance of specific objects. It can be used with objects of vastly different shapes and appearances, and in different backgrounds. Compared to state-of-the-art, we demonstrate a significant improvement on two different datasets which include a total of eleven objects, cluttered background, and heavy occlusion.},
	isbn = {9781467383912},
	issn = {15505499},
	doi = {10.1109/ICCV.2015.115},
	author = {Krull, Alexander and Brachmann, Eric and Michel, Frank and Yang, Michael Ying and Gumhold, Stefan and Carsten Rother}
}
