3D Point Cloud Denoising Algorithm Based on Two-Stage Filtering.

This research introduces a method for smoothing 3D Point Clouds, which is a two-stage algorithm designed for models that are affected by Gaussian noise. The first stage utilizes a median filter to arrange the positions of neighboring points and determine the most probable relative position. The second stage applies a bilateral filter to achieve surface smoothing. This algorithm adds a low-complexity median filter to the original bilateral filter, which can improve the Peak Signal-to-Noise Ratio (PSNR) by 14%, and reduce the Mean Squared Error (MSE) by 75%. Furthermore, this research preserves both the complexity of the bilateral filter and the number of neighboring point searches. This study represents a valuable contribution to the field of 3D Point Cloud smoothing technology.