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

• T. Leistner¹
• H. Schilling¹
• R. Mackowiak²
• S. Gumhold³
• C. Rother¹

• Heidelberg University¹
• Robert Bosch GmbH²
• TU Dresden³

This paper was published at 3DV 2019 with an oral presentation. Download slides and poster.

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 "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.

Results

Code

Download or fork my implementation of EPI-Shift on GitHub.

Citation

@inproceedings{leistner2019learning,
    title={Learning to think outside the box: Wide-baseline light field depth estimation with EPI-shift},
author={Leistner, Titus and Schilling, Hendrik and Mackowiak, Radek and Gumhold, Stefan and Rother, Carsten},
    booktitle={2019 International Conference on 3D Vision (3DV)},
    pages={249--257},
    year={2019},
    organization={IEEE}
}