  Multi-view photometric stereo (MVPS) is a preferred method for detailed and
precise 3D acquisition of an object from images. Although popular methods for
MVPS can provide outstanding results, they are often complex to execute and
limited to isotropic material objects. To address such limitations, we present
a simple, practical approach to MVPS, which works well for isotropic as well as
other object material types such as anisotropic and glossy. The proposed
approach in this paper exploits the benefit of uncertainty modeling in a deep
neural network for a reliable fusion of photometric stereo (PS) and multi-view
stereo (MVS) network predictions. Yet, contrary to the recently proposed
state-of-the-art, we introduce neural volume rendering methodology for a
trustworthy fusion of MVS and PS measurements. The advantage of introducing
neural volume rendering is that it helps in the reliable modeling of objects
with diverse material types, where existing MVS methods, PS methods, or both
may fail. Furthermore, it allows us to work on neural 3D shape representation,
which has recently shown outstanding results for many geometric processing
tasks. Our suggested new loss function aims to fits the zero level set of the
implicit neural function using the most certain MVS and PS network predictions
coupled with weighted neural volume rendering cost. The proposed approach shows
state-of-the-art results when tested extensively on several benchmark datasets.
