  We present PVSeRF, a learning framework that reconstructs neural radiance
fields from single-view RGB images, for novel view synthesis. Previous
solutions, such as pixelNeRF, rely only on pixel-aligned features and suffer
from feature ambiguity issues. As a result, they struggle with the
disentanglement of geometry and appearance, leading to implausible geometries
and blurry results. To address this challenge, we propose to incorporate
explicit geometry reasoning and combine it with pixel-aligned features for
radiance field prediction. Specifically, in addition to pixel-aligned features,
we further constrain the radiance field learning to be conditioned on i)
voxel-aligned features learned from a coarse volumetric grid and ii) fine
surface-aligned features extracted from a regressed point cloud. We show that
the introduction of such geometry-aware features helps to achieve a better
disentanglement between appearance and geometry, i.e. recovering more accurate
geometries and synthesizing higher quality images of novel views. Extensive
experiments against state-of-the-art methods on ShapeNet benchmarks demonstrate
the superiority of our approach for single-image novel view synthesis.
