  This paper presents a simple and effective solution to the longstanding
classical multi-view photometric stereo (MVPS) problem. It is well-known that
photometric stereo (PS) is excellent at recovering high-frequency surface
details, whereas multi-view stereo (MVS) can help remove the low-frequency
distortion due to PS and retain the global geometry of the shape. This paper
proposes an approach that can effectively utilize such complementary strengths
of PS and MVS. Our key idea is to combine them suitably while considering the
per-pixel uncertainty of their estimates. To this end, we estimate per-pixel
surface normals and depth using an uncertainty-aware deep-PS network and
deep-MVS network, respectively. Uncertainty modeling helps select reliable
surface normal and depth estimates at each pixel which then act as a true
representative of the dense surface geometry. At each pixel, our approach
either selects or discards deep-PS and deep-MVS network prediction depending on
the prediction uncertainty measure. For dense, detailed, and precise inference
of the object's surface profile, we propose to learn the implicit neural shape
representation via a multilayer perceptron (MLP). Our approach encourages the
MLP to converge to a natural zero-level set surface using the confident
prediction from deep-PS and deep-MVS networks, providing superior dense surface
reconstruction. Extensive experiments on the DiLiGenT-MV benchmark dataset show
that our method provides high-quality shape recovery with a much lower memory
footprint while outperforming almost all of the existing approaches.
