Abstract: A denoising method based on a multiscale distribution score for a point cloud includes: constructing a two-layer network model based on multiscale perturbation and point cloud distribution, where the two-layer network model includes a feature extraction module for extracting a feature of the point cloud and a displacement prediction module for predicting a displacement of a noise point; constructing a point cloud noise model for improving a denoising effect and retaining a sharp feature and avoiding reducing quality of point cloud data; extracting a global feature h by inputting the point cloud data into the feature extraction module; iteratively learning the displacement of the noise point by the displacement prediction module according to a feature obtained by the feature extraction unit; and defining a loss function of network training, and completing convergence under the condition that the loss function reaches a set threshold or a maximum number of iterations.

Abstract: A denoising method based on a multiscale distribution score for a point cloud includes: constructing a two-layer network model based on multiscale perturbation and point cloud distribution, where the two-layer network model includes a feature extraction module for extracting a feature of the point cloud and a displacement prediction module for predicting a displacement of a noise point; constructing a point cloud noise model for improving a denoising effect and retaining a sharp feature and avoiding reducing quality of point cloud data; extracting a global feature h by inputting the point cloud data into the feature extraction module; iteratively learning the displacement of the noise point by the displacement prediction module according to a feature obtained by the feature extraction unit; and defining a loss function of network training, and completing convergence under the condition that the loss function reaches a set threshold or a maximum number of iterations.