Method for point cloud up-sampling based on deep learning
US11880959B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 30, 2020 |
| Grant date | Jan 23, 2024 |
| Priority date | — |
| Expiry date | Nov 4, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2219/2016
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present invention discloses a method for point cloud up-sampling based on deep learning, including: obtaining training data including a first number of sparse input points and a second number of dense input points; constructing a deep network model to be used for respectively performing replication and sampling operation based on curvature on initial eigenvectors extracted from the first number of sparse input points to obtain a second number of intermediate eigenvectors, performing splicing operation on each intermediate eigenvector, inputting the spliced intermediate eigenvectors into a multilayer perceptron, and determining sampling prediction points based on the sampling eigenvectors output by the multilayer perceptron; training the deep network model until an objective function determined by the sampling prediction points and the dense input points converges; and testing the deep network model to obtain point cloud data of an object under test after up-sampling.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.