  Neural rendering of implicit surfaces performs well in 3D vision
applications. However, it requires dense input views as supervision. When only
sparse input images are available, output quality drops significantly due to
the shape-radiance ambiguity problem. We note that this ambiguity can be
constrained when a 3D point is visible in multiple views, as is the case in
multi-view stereo (MVS). We thus propose to regularize neural rendering
optimization with an MVS solution. The use of an MVS probability volume and a
generalized cross entropy loss leads to a noise-tolerant optimization process.
In addition, neural rendering provides global consistency constraints that
guide the MVS depth hypothesis sampling and thus improves MVS performance.
Given only three sparse input views, experiments show that our method not only
outperforms generic neural rendering models by a large margin but also
significantly increases the reconstruction quality of MVS models. Project page:
https://hao-yu-wu.github.io/s-volsdf/.
