PN-Internet: Point-and-Normal Interactive Network for Noisy Point Clouds

Point cloud denoising and normal estimation are two fundamental yet dependent problems in digital geometry processing. However, both are often independently researched, leading to inconsistent geometry on 3-D surfaces. To address it, we propose point-and-normal interactive network (PN-Internet), an end-to-end PN-Internet work for joint point cloud denoising and normal estimation. PN-Internet leverages the geometric dependency between point positions and normals to design two interactive graph convolutional networks (GCNs): a point-to-normal network and a normal-to-point network. It adopts a coarse-to-fine learning paradigm, where two GCNs are exploited to, respectively, perform point cloud denoising and normal estimation. The point-to-normal network improves the quality of the normals using an MLP module, while the normal-to-point network refines the point positions using a parameter-free projection module based on the constraints from the normals. In addition, we introduce a feature-aware loss function to preserve the quality of 3-D shape features. Unlike most existing methods, PN-Internet takes advantage of the geometric dependency between points and normals and benefits from training data. Our experimental results demonstrate that PN-Internet achieves geometric consistency between point cloud denoising and normal estimation. Furthermore, we show significant improvements over state-of-the-art methods.