Dataset generation method for self-supervised learning scene point cloud completion based on panoramas
US11875583B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 23, 2021 |
| Grant date | Jan 16, 2024 |
| Priority date | — |
| Expiry date | Aug 4, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20081
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present invention belongs to the technical field of 3D reconstruction in the field of computer vision, and provides a dataset generation method for self-supervised learning scene point cloud completion based on panoramas. Pairs of incomplete point cloud and target point cloud with RGB information and normal information can be generated by taking RGB panoramas, depth panoramas and normal panoramas in the same view as input for constructing a self-supervised learning dataset for training of the scene point cloud completion network. The key points of the present invention are occlusion prediction and equirectangular projection based on view conversion, and processing of the stripe problem and point-to-point occlusion problem during conversion. The method of the present invention includes simplification of the collection mode of the point cloud data in a real scene; occlusion prediction idea of view conversion; and design of view selection strategy.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.