  In this paper, we present a Geometry-aware Neural Interpolation (Geo-NI)
framework for light field rendering. Previous learning-based approaches either
rely on the capability of neural networks to perform direct interpolation,
which we dubbed Neural Interpolation (NI), or explore scene geometry for novel
view synthesis, also known as Depth Image-Based Rendering (DIBR). Instead, we
incorporate the ideas behind these two kinds of approaches by launching the NI
with a novel DIBR pipeline. Specifically, the proposed Geo-NI first performs NI
using input light field sheared by a set of depth hypotheses. Then the DIBR is
implemented by assigning the sheared light fields with a novel reconstruction
cost volume according to the reconstruction quality under different depth
hypotheses. The reconstruction cost is interpreted as a blending weight to
render the final output light field by blending the reconstructed light fields
along the dimension of depth hypothesis. By combining the superiorities of NI
and DIBR, the proposed Geo-NI is able to render views with large disparity with
the help of scene geometry while also reconstruct non-Lambertian effect when
depth is prone to be ambiguous. Extensive experiments on various datasets
demonstrate the superior performance of the proposed geometry-aware light field
rendering framework.
