Robust Feature-Preserving Denoising of 3D Point Clouds

Abstract

The increased availability of point cloud data in recent years has lead to a concomitant requirement for high quality denoising methods. This is particularly the case with data obtained using depth cameras or from multi-view stereo reconstruction as both approaches result in noisy point clouds and include significant outliers. Most of the available denoising methods in the literature are not sufficiently robust to outliers and/or are unable to preserve fine-scale 3D features in the denoised representations. In this paper we propose an approach to point cloud denoising that is both robust to outliers and capable of preserving fine-scale 3D features. We identify and remove outliers by utilising a dissimilarity measure based on point positions and their corresponding normals. Subsequently, we use a robust approach to estimate surface point positions in a manner designed to preserve sharp and fine-scale 3D features. We demonstrate the efficacy of our approach and compare with similar methods in the literature by means of experiments on synthetic and real data including large-scale 3D reconstructions of heritage monuments.