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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2023.3316992
|2 doi
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|a eng
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| 100 |
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|a Suhail, Mohammed
|e verfasserin
|4 aut
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| 245 |
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|a Light Field Neural Rendering
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|c 2023
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|a Text
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|a ƒaComputermedien
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|a Date Revised 12.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a Classical light field rendering for novel view synthesis can accurately reproduce view-dependent effects such as reflection, refraction, and translucency, but requires a dense view sampling of the scene. Methods based on geometric reconstruction need only sparse views, but cannot accurately model non-Lambertian effects. We introduce a model that combines the strengths and mitigates the limitations of these two directions. By operating on a four-dimensional representation of the light field, our model learns to represent view-dependent effects accurately. By enforcing geometric constraints during training and inference, the scene geometry is implicitly learned from a sparse set of views. Concretely, we introduce a two-stage transformer-based model that first aggregates features along epipolar lines, then aggregates features along reference views to produce the color of a target ray. Additionally, we propose modifications that allow the model to generalize to scenes without any fine-tuning. Our model outperforms the state-of-the-art on multiple forward-facing and 360 ° datasets, with larger margins on scenes with severe view-dependent variations. Code and results can be found at https://light-field-neural-rendering.github.io/
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|a Journal Article
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|a Esteves, Carlos
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sigal, Leonid
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Makadia, Ameesh
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g PP(2023) vom: 27. Sept.
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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|g volume:PP
|g year:2023
|g day:27
|g month:09
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|u http://dx.doi.org/10.1109/TPAMI.2023.3316992
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