3D point cloud encoding and decoding method, compression method and device based on graph dictionary learning
US12046009B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 29, 2024 |
| Grant date | Jul 23, 2024 |
| Priority date | — |
| Expiry date | Feb 29, 2044 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02T10/40
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A graph dictionary learning method for a 3D point cloud comprises: obtaining N point clouds to form training dataset; performing voxelization process on the point cloud data to obtain voxelized point cloud data of the training dataset; performing voxel block division on the point cloud data of the training dataset, selecting a plurality of voxel blocks as the training dataset, and constructing a graph dictionary learning model according to the training dataset; and performing iterative optimization on the graph dictionary learning objective function to obtain a graph dictionary for encoding and decoding a 3D point cloud signal. The present disclosure effectively uses the spatial correlation between point cloud signals to near-optimally remove the redundancy among point cloud signals.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.