  This study proposes a neural disparity field (NDF) that establishes an
implicit, continuous representation of scene disparity based on a neural field
and an iterative approach to address the inverse problem of NDF reconstruction
from light-field data. NDF enables seamless and precise characterization of
disparity variations in three-dimensional scenes and can discretize disparity
at any arbitrary resolution, overcoming the limitations of traditional
disparity maps that are prone to sampling errors and interpolation
inaccuracies. The proposed NDF network architecture utilizes hash encoding
combined with multilayer perceptrons to capture detailed disparities in texture
levels, thereby enhancing its ability to represent the geometric information of
complex scenes. By leveraging the spatial-angular consistency inherent in
light-field data, a differentiable forward model to generate a central view
image from the light-field data is developed. Based on the forward model, an
optimization scheme for the inverse problem of NDF reconstruction using
differentiable propagation operators is established. Furthermore, an iterative
solution method is adopted to reconstruct the NDF in the optimization scheme,
which does not require training datasets and applies to light-field data
captured by various acquisition methods. Experimental results demonstrate that
high-quality NDF can be reconstructed from light-field data using the proposed
method. High-resolution disparity can be effectively recovered by NDF,
demonstrating its capability for the implicit, continuous representation of
scene disparities.
