ume density could be defined to represent the underlying 3D
geometry. Current practice often relies on heuristic thresh-
olding on the density values; due to insufficient constraints
on the level sets, however, such surfaces are often noisy
and may not model the scene structures accurately [36, 41].
Therefore, more direct modeling of surfaces is preferred for
photogrammetric surface reconstruction problems.
Neural surface reconstruction. For scene representations
with better-defined 3D surfaces, implicit functions such as
occupancy grids [24, 25] or SDFs [42] are preferred over
simple volume density fields. To integrate with neural vol-
ume rendering [22], different techniques [36, 41] have been
proposed to reparametrize the underlying representations
back to volume density. These designs of neural implicit
functions enable more accurate surface prediction with view
synthesis capabilities of unsacrificed quality [42].
Neuralangelo- High-Fidelity Neural Surface Reconstruction