Article ; Online: A Generative Model for Generic Light Field Reconstruction.
IEEE transactions on pattern analysis and machine intelligence
2022 Volume 44, Issue 4, Page(s) 1712–1724
Abstract: Recently deep generative models have achieved impressive progress in modeling the distribution of training data. In this work, we present for the first time a generative model for 4D light field patches using variational autoencoders to capture the data ... ...
Abstract | Recently deep generative models have achieved impressive progress in modeling the distribution of training data. In this work, we present for the first time a generative model for 4D light field patches using variational autoencoders to capture the data distribution of light field patches. We develop a generative model conditioned on the central view of the light field and incorporate this as a prior in an energy minimization framework to address diverse light field reconstruction tasks. While pure learning-based approaches do achieve excellent results on each instance of such a problem, their applicability is limited to the specific observation model they have been trained on. On the contrary, our trained light field generative model can be incorporated as a prior into any model-based optimization approach and therefore extend to diverse reconstruction tasks including light field view synthesis, spatial-angular super resolution and reconstruction from coded projections. Our proposed method demonstrates good reconstruction, with performance approaching end-to-end trained networks, while outperforming traditional model-based approaches on both synthetic and real scenes. Furthermore, we show that our approach enables reliable light field recovery despite distortions in the input. |
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Language | English |
Publishing date | 2022-03-04 |
Publishing country | United States |
Document type | Journal Article |
ISSN | 1939-3539 |
ISSN (online) | 1939-3539 |
DOI | 10.1109/TPAMI.2020.3039841 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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