Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/34291
Autoria: | Zubair, M. Nunes, P. Conti, C. Soares, L. D. |
Data: | 2024 |
Título próprio: | Light field view synthesis using deformable convolutional neural networks |
Título e volume do livro: | 2024 Picture Coding Symposium, PCS 2024, Proceedings |
Paginação: | 1 - 5 |
Título do evento: | 2024 Picture Coding Symposium, PCS 2024 |
Referência bibliográfica: | Zubair, M., Nunes, P., Conti, C., & Soares, L. D. (2024). Light field view synthesis using deformable convolutional neural networks. 2024 Picture Coding Symposium, PCS 2024, Proceedings. IEEE. https://doi.org/10.1109/PCS60826.2024.10566360 |
ISBN: | 979-835035848-3 |
DOI (Digital Object Identifier): | 10.1109/PCS60826.2024.10566360 |
Palavras-chave: | Light field view synthesis Deformable convolution Depth-wise separable convolution Geometry-aware network |
Resumo: | 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. |
Arbitragem científica: | yes |
Acesso: | Acesso Aberto |
Aparece nas coleções: | IT-CRI - Comunicações a conferências internacionais |
Ficheiros deste registo:
Ficheiro | Tamanho | Formato | |
---|---|---|---|
conferenceObject_104512.pdf Restricted Access | 1,6 MB | Adobe PDF | Ver/Abrir Request a copy |
Todos os registos no repositório estão protegidos por leis de copyright, com todos os direitos reservados.