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Zubair, M., Nunes, P., Conti, C. & Soares, L. D. (2024). Light Field View Synthesis Using Deformable Convolutional Neural Networks. 2024 Picture Coding Symposium (PCS).

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

@misc{zubair2024_1736689530510,
	author = "Zubair, M. and Nunes, P. and Conti, C. and Soares, L. D.",
	title = "Light Field View Synthesis Using Deformable Convolutional Neural Networks",
	year = "2024",
	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
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  -