Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift

TitleLearning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift
Publication TypeConference Paper
Year of Publication2019
AuthorsLeistner, T, Schilling, H, Mackowiak, R, Gumhold, S, Rother, C
Conference NameProceedings - 2019 International Conference on 3D Vision, 3DV 2019
Date Publishedsep
ISBN Number9781728131313
KeywordsComputer vision, deep learning, depth estimation, light fields, Stereo

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 '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.

URLhttp://arxiv.org/abs/1909.09059 http://dx.doi.org/10.1109/3DV.2019.00036
Citation KeyLeistner2019