GeoGCN: Geometric Dual-domain Graph Convolution Network for Point Cloud Denoising
This addresses the problem of noise in point clouds for applications like 3D scanning and computer vision, representing a novel paradigm rather than an incremental improvement.
The paper tackles point cloud denoising by proposing GeoGCN, a geometric dual-domain graph convolution network that introduces Real Normal and Virtual Normal to exploit geometric information, resulting in improved noise-robustness and feature preservation compared to state-of-the-art methods.
We propose GeoGCN, a novel geometric dual-domain graph convolution network for point cloud denoising (PCD). Beyond the traditional wisdom of PCD, to fully exploit the geometric information of point clouds, we define two kinds of surface normals, one is called Real Normal (RN), and the other is Virtual Normal (VN). RN preserves the local details of noisy point clouds while VN avoids the global shape shrinkage during denoising. GeoGCN is a new PCD paradigm that, 1) first regresses point positions by spatialbased GCN with the help of VNs, 2) then estimates initial RNs by performing Principal Component Analysis on the regressed points, and 3) finally regresses fine RNs by normalbased GCN. Unlike existing PCD methods, GeoGCN not only exploits two kinds of geometry expertise (i.e., RN and VN) but also benefits from training data. Experiments validate that GeoGCN outperforms SOTAs in terms of both noise-robustness and local-and-global feature preservation.