  This paper studies implicit surface reconstruction leveraging differentiable
ray casting. Previous works such as IDR and NeuS overlook the spatial context
in 3D space when predicting and rendering the surface, thereby may fail to
capture sharp local topologies such as small holes and structures. To mitigate
the limitation, we propose a flexible neural implicit representation leveraging
hierarchical voxel grids, namely Neural Deformable Anchor (NeuDA), for
high-fidelity surface reconstruction. NeuDA maintains the hierarchical anchor
grids where each vertex stores a 3D position (or anchor) instead of the direct
embedding (or feature). We optimize the anchor grids such that different local
geometry structures can be adaptively encoded. Besides, we dig into the
frequency encoding strategies and introduce a simple hierarchical positional
encoding method for the hierarchical anchor structure to flexibly exploit the
properties of high-frequency and low-frequency geometry and appearance.
Experiments on both the DTU and BlendedMVS datasets demonstrate that NeuDA can
produce promising mesh surfaces.
