Point Cloud Denoising Algorithm Combined with Improved Radius Filtering and Local Plane Fitting

Point cloud denoising is crucial for ensuring the quality of three-dimensional point clouds. However, existing denoising methods are extremely prone to error removal for object point clouds while removing noise points, and the error increases with the improvement of noise recognition accuracy. To address this issue, a point cloud denoising algorithm that incorporates improved radius filtering and local plane fitting is proposed. To achieve effective noise point removal, noise points are divided into far- and near-noise points based on their Euclidean distance from the object point clouds and are successively processed using different denoising strategies. First, the far-noise points are removed using improved radius filtering based on the density characteristics of the point clouds. Next, the near-noise points, which are closely located to the object point clouds and attached to their surfaces, are removed using a geometrical feature assessing the deviation of the point cloud from the local fitting plane. Finally, experiments are conducted on common point cloud datasets and the proposed method is validated by comparing its performance with that of three other advanced methods. The results show that the proposed method outperforms all three methods in all indexes under the same noise level. Our proposed method effectively improves the object point cloud recognition accuracy while achieving higher noise recognition accuracy, with the denoising accuracy reaching 95.9%.