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.

利用邻域多分辨率格点的特征编码法在学习基于隐式场景表示中发挥了重要作用，然而先前的技术没有明确地利用大多数物体或场景的表面法线和局部平滑性特征。本文提出了第一个明确地利用了这些三维特征的三维几何编码器，通过使用新颖的圆柱体积插值来建模局部平面不变性。此外，我们明确地结合了局部特征聚合来对圆柱体插值特征进行正则化和平滑处理。我们在 ABC、Thingi10k、ShapeNet 和 Matterport3D 上评估了我们的方法，用于对象和场景表示。与使用常规格点的方法相比，我们的几何编码器显示出更快的收敛速度，并获得更清晰的三维表面。与先前的技术相比，我们的方法取得了最先进的结果。