Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based
  View Synthesis
Christian Reiser Stephan Garbin Pratul P. Srinivasan Dor Verbin Richard Szeliski Ben Mildenhall Jonathan T. Barron Peter Hedman Andreas Geiger
While surface-based view synthesis algorithms are appealing due to their low
computational requirements, they often struggle to reproduce thin structures.
In contrast, more expensive methods that model the scene's geometry as a
volumetric density field (e.g. NeRF) excel at reconstructing fine geometric
detail. However, density fields often represent geometry in a "fuzzy" manner,
which hinders exact localization of the surface. In this work, we modify
density fields to encourage them to converge towards surfaces, without
compromising their ability to reconstruct thin structures. First, we employ a
discrete opacity grid representation instead of a continuous density field,
which allows opacity values to discontinuously transition from zero to one at
the surface. Second, we anti-alias by casting multiple rays per pixel, which
allows occlusion boundaries and subpixel structures to be modelled without
using semi-transparent voxels. Third, we minimize the binary entropy of the
opacity values, which facilitates the extraction of surface geometry by
encouraging opacity values to binarize towards the end of training. Lastly, we
develop a fusion-based meshing strategy followed by mesh simplification and
appearance model fitting. The compact meshes produced by our model can be
rendered in real-time on mobile devices and achieve significantly higher view
synthesis quality compared to existing mesh-based approaches.