  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.

Computational Geometry