Novel method for robust bilateral filtering point cloud denoising

Abstract

Bilateral filtering for point cloud denoising relies on the normal of the points. However, normals are not inherent attributes of point clouds but are estimated using algorithms such as principal component analysis (PCA). Due to inaccuracies in the normals, bilateral filtering may lead to unexpected results, such as smoothing in sharp regions. This study addressed this issue by improving the accuracy of normals. Initially, PCA was employed to estimate the initial normals of the point cloud. The points were then categorized into edge and planar points by fitting a three-dimensional sphere. Subsequently, iterative weighted PCA was applied to refine the normals of the edge points. Finally, the improved normals were integrated into bilateral filtering to achieve point cloud denoising. Additionally, a robust estimator was used instead of a Gaussian function to compute the weights in the bilateral filtering. To assess and compare the performance of this algorithm with related ones, the mean square angular error (MSAE) and angular error distribution (AE) were employed to evaluate the accuracy of normal estimation, while the mean square error (MSE) and signal-to-noise ratio (SNR) were employed to assess the denoising performance. Results indicate that the proposed method yielded the most accurate normals compared to PCA, Jet, and VCM normal estimation algorithms. Moreover, when compared to algebraic point set surfaces (APSS), robust implicit moving least squares (RIMLS), anisotropic weighted locally optimal projection (AWLOP), bilateral filtering, and guided filtering point cloud denoising algorithms, the proposed method consistently achieved the smallest MSE and the highest SNR in most cases on the dataset used in this study.