  In this paper, we tackle the problem of dense light field (LF) reconstruction
from sparsely-sampled ones with wide baselines and propose a learnable model,
namely dynamic interpolation, to replace the commonly-used geometry warping
operation. Specifically, with the estimated geometric relation between input
views, we first construct a lightweight neural network to dynamically learn
weights for interpolating neighbouring pixels from input views to synthesize
each pixel of novel views independently. In contrast to the fixed and
content-independent weights employed in the geometry warping operation, the
learned interpolation weights implicitly incorporate the correspondences
between the source and novel views and adapt to different image content
information. Then, we recover the spatial correlation between the independently
synthesized pixels of each novel view by referring to that of input views using
a geometry-based spatial refinement module. We also constrain the angular
correlation between the novel views through a disparity-oriented LF structure
loss. Experimental results on LF datasets with wide baselines show that the
reconstructed LFs achieve much higher PSNR/SSIM and preserve the LF parallax
structure better than state-of-the-art methods. The source code is publicly
available at https://github.com/MantangGuo/DI4SLF.
