  We present a modern solution to the multi-view photometric stereo problem
(MVPS). Our work suitably exploits the image formation model in a MVPS
experimental setup to recover the dense 3D reconstruction of an object from
images. We procure the surface orientation using a photometric stereo (PS)
image formation model and blend it with a multi-view neural radiance field
representation to recover the object's surface geometry. Contrary to the
previous multi-staged framework to MVPS, where the position, iso-depth
contours, or orientation measurements are estimated independently and then
fused later, our method is simple to implement and realize. Our method performs
neural rendering of multi-view images while utilizing surface normals estimated
by a deep photometric stereo network. We render the MVPS images by considering
the object's surface normals for each 3D sample point along the viewing
direction rather than explicitly using the density gradient in the volume space
via 3D occupancy information. We optimize the proposed neural radiance field
representation for the MVPS setup efficiently using a fully connected deep
network to recover the 3D geometry of an object. Extensive evaluation on the
DiLiGenT-MV benchmark dataset shows that our method performs better than the
approaches that perform only PS or only multi-view stereo (MVS) and provides
comparable results against the state-of-the-art multi-stage fusion methods.
