  This paper introduces a novel paradigm for the generalizable neural radiance
field (NeRF). Previous generic NeRF methods combine multiview stereo techniques
with image-based neural rendering for generalization, yielding impressive
results, while suffering from three issues. First, occlusions often result in
inconsistent feature matching. Then, they deliver distortions and artifacts in
geometric discontinuities and locally sharp shapes due to their individual
process of sampled points and rough feature aggregation. Third, their
image-based representations experience severe degradations when source views
are not near enough to the target view. To address challenges, we propose the
first paradigm that constructs the generalizable neural field based on
point-based rather than image-based rendering, which we call the Generalizable
neural Point Field (GPF). Our approach explicitly models visibilities by
geometric priors and augments them with neural features. We propose a novel
nonuniform log sampling strategy to improve both rendering speed and
reconstruction quality. Moreover, we present a learnable kernel spatially
augmented with features for feature aggregations, mitigating distortions at
places with drastically varying geometries. Besides, our representation can be
easily manipulated. Experiments show that our model can deliver better
geometries, view consistencies, and rendering quality than all counterparts and
benchmarks on three datasets in both generalization and finetuning settings,
preliminarily proving the potential of the new paradigm for generalizable NeRF.
