  We propose LIRF (Local Implicit Ray Function), a generalizable neural
rendering approach for novel view rendering. Current generalizable neural
radiance fields (NeRF) methods sample a scene with a single ray per pixel and
may therefore render blurred or aliased views when the input views and rendered
views capture scene content with different resolutions. To solve this problem,
we propose LIRF to aggregate the information from conical frustums to construct
a ray. Given 3D positions within conical frustums, LIRF takes 3D coordinates
and the features of conical frustums as inputs and predicts a local volumetric
radiance field. Since the coordinates are continuous, LIRF renders high-quality
novel views at a continuously-valued scale via volume rendering. Besides, we
predict the visible weights for each input view via transformer-based feature
matching to improve the performance in occluded areas. Experimental results on
real-world scenes validate that our method outperforms state-of-the-art methods
on novel view rendering of unseen scenes at arbitrary scales.
