  We propose an end-to-end inverse rendering pipeline called SupeRVol that
allows us to recover 3D shape and material parameters from a set of color
images in a super-resolution manner. To this end, we represent both the
bidirectional reflectance distribution function (BRDF) and the signed distance
function (SDF) by multi-layer perceptrons. In order to obtain both the surface
shape and its reflectance properties, we revert to a differentiable volume
renderer with a physically based illumination model that allows us to decouple
reflectance and lighting. This physical model takes into account the effect of
the camera's point spread function thereby enabling a reconstruction of shape
and material in a super-resolution quality. Experimental validation confirms
that SupeRVol achieves state of the art performance in terms of inverse
rendering quality. It generates reconstructions that are sharper than the
individual input images, making this method ideally suited for 3D modeling from
low-resolution imagery.
