Point cloud denoising method based on neighborhood radius and gravitation analysis

Point cloud denoising presents several complex challenges. First, most existing denoising methods are capable of addressing either large-scale or small-scale noise, but not both effectively. Second, the parameters in various algorithms are often difficult to fine-tune. Third, certain noise points are so proximate to normal points that they are challenging to distinguish and remove. To tackle these issues, we introduce a point cloud denoising approach based on the analysis of a point's neighborhood radius and gravitation. Through the analysis of a point's neighborhood radius, this method can automatically discern the presence of large-scale noise within the point cloud. For the point cloud with large-scale and small-scale noise, this method can separate the large-scale noise by analyzing the distribution characteristics of the radius of a point's neighborhood, leaving only small-scale noise to be addressed. For small-scale noise, the gravitation analysis method first analyzes the gravitation force on each doubtful point and then uses the force values to ascertain whether a point is indeed noise. Therefore, it can automatically identify and deal with point clouds with different noise scales. Experiments show this method can effectively remove multi-scale noise, and the average noise removal accuracy exceeds 98%. In addition, the absence of a need for manual parameter-setting enhances its practicality.