@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}
}
@article {Arnab2018,
	title = {Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation},
	journal = {Cvpr},
	volume = {XX},
	number = {Xx},
	year = {2018},
	pages = {1{\textendash}15},
	abstract = {{\textemdash}Semantic Segmentation is the task of labelling every pixel in an image with a pre-defined object category. It has numer-ous applications in scenarios where the detailed understanding of an image is required, such as in autonomous vehicles and medical diagnosis. This problem has traditionally been solved with probabilistic models known as Conditional Random Fields (CRFs) due to their ability to model the relationships between the pixels being predicted. However, Deep Neural Networks (DNNs) have recently been shown to excel at a wide range of computer vision problems due to their ability to learn rich feature representations automatically from data, as opposed to traditional hand-crafted features. The idea of combining CRFs and DNNs have achieved state-of-the-art results in a number of domains. We review the literature on combining the modelling power of CRFs with the representation-learning ability of DNNs, ranging from early work that combines these two techniques as independent stages of a common pipeline to recent approaches that embed inference of probabilistic models directly in the neural network itself. Finally, we summarise future research directions.},
	keywords = {conditional random fields, deep learning, seman-},
	url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.308.8889\&rep=rep1\&type=pdf\%0Ahttp://dx.doi.org/10.1109/CVPR.2012.6248050},
	author = {Arnab, Anurag and Zheng, Shuai and Jayasumana, Sadeep and Romera-paredes, Bernardino and Kirillov, Alexander and Savchynskyy, Bogdan and Carsten Rother and Kahl, Fredrik and Torr, Philip}
}
