  Encoding 3D points is one of the primary steps in learning-based implicit
scene representation. Using features that gather information from neighbors
with multi-resolution grids has proven to be the best geometric encoder for
this task. However, prior techniques do not exploit some characteristics of
most objects or scenes, such as surface normals and local smoothness. This
paper is the first to exploit those 3D characteristics in 3D geometric encoders
explicitly. In contrast to prior work on using multiple levels of details,
regular cube grids, and trilinear interpolation, we propose 3D-oriented grids
with a novel cylindrical volumetric interpolation for modeling local planar
invariance. In addition, we explicitly include a local feature aggregation for
feature regularization and smoothing of the cylindrical interpolation features.
We evaluate our approach on ABC, Thingi10k, ShapeNet, and Matterport3D, for
object and scene representation. Compared to the use of regular grids, our
geometric encoder is shown to converge in fewer steps and obtain sharper 3D
surfaces. When compared to the prior techniques, our method gets
state-of-the-art results.
