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Communication Dans Un Congrès Année : 2023

Joint NeuraL Representation For Multiple Light Fields

Résumé

Neural implicit representations have appeared as a promising technique for representing a variety of signals, among which light fields, offering several advantages over traditional gridbased representations, such as independence to the signal resolution. While some work has been done to find good initial representations for a given type of signal, usually via metalearning approaches, exploiting the features shared between different scenes remains an understudied problem. We provide a step towards this end by presenting a method for sharing the representation between thousands of light fields, splitting the representation between a part that is shared between all light fields and a part which varies individually from one light field to another. We show that this joint representation possesses good interpolation properties, and allows for a more lightweight storage of a whole database of light fields, exhibiting a tenfold reduction in the size of the representation when compared to using a separate representation for each light field.
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Dates et versions

hal-04054325 , version 1 (31-03-2023)

Identifiants

  • HAL Id : hal-04054325 , version 1

Citer

Guillaume Le Guludec, Christine Guillemot. Joint NeuraL Representation For Multiple Light Fields. ICASSP 2023 - IEEE Internal Conference on Acoustics, Speech and Signal Processing, Jun 2023, Rhodes, Greece. pp.1-5. ⟨hal-04054325⟩
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