In this paper, we propose PCPNet, a deep-learning based approach for
estimating local 3D shape properties in point clouds. In contrast to the
majority of prior techniques that concentrate on global or mid-level
attributes, e.g., for shape classification or semantic labeling, we suggest a
patch-based learning method, in which a series of local patches at multiple
scales around each point is encoded in a structured manner. Our approach is
especially well-adapted for estimating local shape properties such as normals
(both unoriented and oriented) and curvature from raw point clouds in the
presence of strong noise and multi-scale features. Our main contributions
include both a novel multi-scale variant of the recently proposed PointNet
architecture with emphasis on local shape information, and a series of novel
applications in which we demonstrate how learning from training data arising
from well-structured triangle meshes, and applying the trained model to noisy
point clouds can produce superior results compared to specialized
state-of-the-art techniques. Finally, we demonstrate the utility of our
approach in the context of shape reconstruction, by showing how it can be used
to extract normal orientation information from point clouds.

本文提出了一种基于深度学习的点云局部三维形状属性估计方法 PCPNet，采用基于 patch 的学习方法，结合多尺度编码，能在强噪声和多尺度特征的情况下更好地估计法线及曲率等局部形状信息。我们的方法在结构化三角网格训练数据上训练，可以在处理噪声点云时得到比现有技术更好的结果，并成功在形状重构方面得到应用。