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Zubair, M., Nunes, P., Conti, C. & Soares, L. D. (2024). Light Field View Synthesis Using Deformable Convolutional Neural Networks. In 2024 Picture Coding Symposium (PCS). (pp. 1-5). Taichung, Taiwan: IEEE.

M. Zubair et al.,  "Light Field View Synthesis Using Deformable Convolutional Neural Networks", in 2024 Picture Coding Symp. (PCS), Taichung, Taiwan, IEEE, 2024, pp. 1-5

@inproceedings{zubair2024_1732212314578,
	author = "Zubair, M. and Nunes, P. and Conti, C. and Soares, L. D.",
	title = "Light Field View Synthesis Using Deformable Convolutional Neural Networks",
	booktitle = "2024 Picture Coding Symposium (PCS)",
	year = "2024",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.1109/PCS60826.2024.10566360",
	pages = "1-5",
	publisher = "IEEE",
	address = "Taichung, Taiwan",
	organization = "",
	url = "https://ieeexplore.ieee.org/document/10566360"
}

TY  - CPAPER
TI  - Light Field View Synthesis Using Deformable Convolutional Neural Networks
T2  - 2024 Picture Coding Symposium (PCS)
AU  - Zubair, M.
AU  - Nunes, P.
AU  - Conti, C.
AU  - Soares, L. D.
PY  - 2024
SP  - 1-5
DO  - 10.1109/PCS60826.2024.10566360
CY  - Taichung, Taiwan
UR  - https://ieeexplore.ieee.org/document/10566360
AB  - Light Field (LF) imaging has emerged as a technology that can simultaneously capture both intensity values and directions of light rays from real-world scenes. Densely sampled
LFs are drawing increased attention for their wide application in 3D reconstruction, depth estimation, and digital refocusing. In order to synthesize additional views to obtain a LF with higher
angular resolution, many learning-based methods have been proposed. This paper follows a similar approach to Liu et al. [1] but using deformable convolutions to improve the view synthesis
performance and depth-wise separable convolutions to reduce the amount of model parameters. The proposed framework consists of two main modules: i) a multi-representation view synthesis
module to extract features from different LF representations of the sparse LF, and ii) a geometry-aware refinement module to synthesize a dense LF by exploring the structural characteristics
of the corresponding sparse LF. Experimental results over various benchmarks demonstrate the superiority of the proposed method when compared to state-of-the-art ones. The code is available at https://github.com/MSP-IUL/deformable lfvs.
ER  -