Point cloud denoising method based on deep learning for aircraft part
US11514555B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 7, 2021 |
| Grant date | Nov 29, 2022 |
| Priority date | — |
| Expiry date | Feb 28, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure provides a point cloud denoising method based on deep learning for an aircraft part, in which different degrees of Gaussian noise are added based on a theoretical data model of the aircraft part, a heightmap for each point in the theoretical data model is generated, and a deep learning training set is constructed. A deep learning network is trained based on the constructed deep learning training set, to obtain a deep learning network model. A real aircraft part is scanned via a laser scanner to obtain measured point cloud data. The normal information of the measured point cloud is predicted based on the trained deep learning network model. Based on the predicted normal information, a position of each point in the measured point cloud data is further updated, thereby completing denoising of the measured point cloud data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.