  Point normal, as an intrinsic geometric property of 3D objects, not only
serves conventional geometric tasks such as surface consolidation and
reconstruction, but also facilitates cutting-edge learning-based techniques for
shape analysis and generation. In this paper, we propose a normal refinement
network, called Refine-Net, to predict accurate normals for noisy point clouds.
Traditional normal estimation wisdom heavily depends on priors such as surface
shapes or noise distributions, while learning-based solutions settle for single
types of hand-crafted features. Differently, our network is designed to refine
the initial normal of each point by extracting additional information from
multiple feature representations. To this end, several feature modules are
developed and incorporated into Refine-Net by a novel connection module.
Besides the overall network architecture of Refine-Net, we propose a new
multi-scale fitting patch selection scheme for the initial normal estimation,
by absorbing geometry domain knowledge. Also, Refine-Net is a generic normal
estimation framework: 1) point normals obtained from other methods can be
further refined, and 2) any feature module related to the surface geometric
structures can be potentially integrated into the framework. Qualitative and
quantitative evaluations demonstrate the clear superiority of Refine-Net over
the state-of-the-arts on both synthetic and real-scanned datasets. Our code is
available at https://github.com/hrzhou2/refinenet.
