A feature-preserving framework for point cloud denoising

• DOI: 10.1016/j.cad.2020.102857
• Publication Date: 2020-05-11T15:47:54Z
• Authors: Zheng Liu; Xiaowen Xiao; Saishang Zhong; Weina Wang; Yanlei Li; Ling Zhang; Zhong Xie
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

Abstract Point cloud denoising has been an attractive problem in geometry processing. The main challenge is to eliminate noise while preserving different levels of features and preventing unnatural effects (such as over-sharpened artifacts on smoothly curved faces and cross artifacts on sharp edges). In this paper, we propose a novel feature-preserving framework to achieve these goals. Firstly, we newly define some discrete operators on point clouds, which can be used to construct a second order regularization for restoring a point normal field. Then, based on the filtered normals, we perform a feature detection step by a bi-tensor voting scheme. As will be seen, it is robust against noise and can locate underlying geometric features accurately. Finally, we reposition points with a multi-normal strategy by using a simple yet effective RANSAC-based algorithm. Intensive experimental results show that the proposed method performs favorably compared to other state-of-the-art approaches.