  Neural Radiance Fields (NeRF) achieve photo-realistic view synthesis with
densely captured input images. However, the geometry of NeRF is extremely
under-constrained given sparse views, resulting in significant degradation of
novel view synthesis quality. Inspired by self-supervised depth estimation
methods, we propose StructNeRF, a solution to novel view synthesis for indoor
scenes with sparse inputs. StructNeRF leverages the structural hints naturally
embedded in multi-view inputs to handle the unconstrained geometry issue in
NeRF. Specifically, it tackles the texture and non-texture regions
respectively: a patch-based multi-view consistent photometric loss is proposed
to constrain the geometry of textured regions; for non-textured ones, we
explicitly restrict them to be 3D consistent planes. Through the dense
self-supervised depth constraints, our method improves both the geometry and
the view synthesis performance of NeRF without any additional training on
external data. Extensive experiments on several real-world datasets demonstrate
that StructNeRF surpasses state-of-the-art methods for indoor scenes with
sparse inputs both quantitatively and qualitatively.
