US 12,469,113 B2
Denoising method based on multiscale distribution score for point cloud
Gang Xiao, Zhejiang (CN); Jiawei Lu, Zhejiang (CN); Qibing Wang, Zhejiang (CN); Chen Li, Zhejiang (CN); Hao Hu, Zhejiang (CN); and Junbo Yao, Zhejiang (CN)
Assigned to China Jiliang University, Zhejiang (CN); and ZHEJIANG UNIVERSITY OF TECHNOLOGY, Zhejiang (CN)
Filed by China Jiliang University, Zhejiang (CN); and ZHEJIANG UNIVERSITY OF TECHNOLOGY, Zhejiang (CN)
Filed on Aug. 7, 2023, as Appl. No. 18/366,604.
Claims priority of application No. 202310184967.9 (CN), filed on Mar. 1, 2023.
Prior Publication US 2024/0296528 A1, Sep. 5, 2024
Int. Cl. G06T 5/70 (2024.01); G06T 5/60 (2024.01)
CPC G06T 5/70 (2024.01) [G06T 5/60 (2024.01); G06T 2207/10028 (2013.01)] 9 Claims
OG exemplary drawing
 
1. A denoising method based on a multiscale distribution score for a point cloud, comprises:
step 1: constructing a two-layer network model, wherein the two-layer network model comprises 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;
step 2: constructing a point cloud noise model for improving a denoising effect and retaining a sharp feature and avoiding reducing quality of point cloud data;
step 3: extracting a global feature h by inputting the point cloud data into the feature extraction module, wherein
preprocessing the point cloud data, enhancing an anti-noise performance of a network by adding multiscale noise perturbation to processed point cloud data, and extracting, with Encoder, the global feature h of the point cloud by the feature extraction module;
step 4: iteratively learning the displacement of the noise point by the displacement prediction module according to a feature obtained by the feature extraction module; and
step 5: defining a loss function of network training, and completing convergence in response to the loss function reaches a set threshold or a maximum number of iterations;
wherein the step 1 further comprises:
preprocessing a neighborhood of an input noisy point cloud by the feature extraction module, and the anti-noise performance of the network is enhanced through the multiscale noise perturbation;
wherein a displacement estimation module of the displacement prediction module obtains a distribution score of a neighborhood point cloud according to a score estimation unit, considers a position of each point, further covers a neighborhood of the point, and finally completes a denoising process by iteratively learning the displacement of the noise point; wherein
wherein the neighborhood point cloud refers to a set of data that have a distance less than a specific distance from a selected point in current point cloud data;
wherein the point cloud distribution refers to that point clouds scattered in a certain area obey a distribution function, wherein the distribution function shows statistical regularity of a random point cloud;
wherein the multiscale noise perturbation refers to use of multiscale isotropic Gaussian noise with a mean value of 0 to interfere with the data.