  Surface reconstruction has been seeing a lot of progress lately by utilizing
Implicit Neural Representations (INRs). Despite their success, INRs often
introduce hard to control inductive bias (i.e., the solution surface can
exhibit unexplainable behaviours), have costly inference, and are slow to
train. The goal of this work is to show that replacing neural networks with
simple grid functions, along with two novel geometric priors achieve comparable
results to INRs, with instant inference, and improved training times. To that
end we introduce VisCo Grids: a grid-based surface reconstruction method
incorporating Viscosity and Coarea priors. Intuitively, the Viscosity prior
replaces the smoothness inductive bias of INRs, while the Coarea favors a
minimal area solution. Experimenting with VisCo Grids on a standard
reconstruction baseline provided comparable results to the best performing INRs
on this dataset.
