The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images
Open accessAbstract
Light field cameras capture both the spatial and the angular properties of light rays in space. Due to its property, one can compute the depth from light fields in uncontrolled lighting environments, which is a big advantage over active sensing devices. Depth computed from light fields can be used for many applications including 3D modelling and refocusing. However, light field images from hand-held cameras have very narrow baselines with noise, making the depth estimation difficult. Many approaches have been proposed to overcome these limitations for the light field depth estimation, but there is a clear trade-off between the accuracy and the speed in these methods. In this paper, we introduce a fast and accurate light field depth estimation method based on a fully-convolutional neural network. Our network is designed by considering the light field geometry and we also overcome the lack of training data by proposing light field specific data augmentation methods. We achieved the top rank in the HCI 4D Light Field Benchmark on most metrics, and we also demonstrate the effectiveness of the proposed method on real-world light-field images.
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Cites
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
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Mayer, N., Ilg, E., Cremers, D., Dosovitskiy, A., & Brox, T. (2016) · Cited by 1,214
High performance imaging using large camera arrays
Wilburn, B., Joshi, N., Vaish, V., Talvala, E.-V., Antunez, E., Barth, A., Adams, A., Horowitz, M., & Levoy, M. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073259 · Cited by 1,042
Efficient Deep Learning for Stereo Matching
Luo, W., Schwing, A. G., & Urtasun, R. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.614 · Cited by 741
Learning-based view synthesis for light field cameras
Kalantari, N. K., Wang, T.-C., & Ramamoorthi, R. (2016) · ACM Transactions on Graphics · doi:10.1145/2980179.2980251 · Cited by 726
A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields
Honauer, K., Johannsen, O., Kondermann, D., & Goldluecke, B. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-54187-7_2 · Cited by 642
Cited by
Data augmentation: A comprehensive survey of modern approaches
Mumuni, A., & Mumuni, F. (2022) · Array · doi:10.1016/j.array.2022.100258 · Cited by 891
Adaptive EPI-Matching Cost for Light Field Disparity Estimation
Wang, T., Sheng, H., Chen, R., Cong, R., Zhao, M., & Cui, Z. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3488147 · Cited by 348
Metadata
- Authors
- Shin, C., Jeon, H.-G., Yoon, Y., Kweon, I. S., & Kim, S. J.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 4748-4757
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2964178790
- Cited by
- 319
- References
- 28
- FWCI
- 8.77
- Type
- conference paper
- Language
- en
- Open access
- green
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- Shin, C., Jeon, H.-G., Yoon, Y., Kweon, I. S., & Kim, S. J. (2018). EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2018.00499
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