Method and apparatus for training 6D pose estimation network based on deep learning iterative matching
US11200696B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 17, 2020 |
| Grant date | Dec 14, 2021 |
| Priority date | — |
| Expiry date | Sep 17, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure relates to a method and an apparatus for training a 6D pose estimation network based on deep learning iterative matching. The method includes: obtaining a rendered image and a first segmentation mask of a target object by using a 3D model and an initial 6D pose estimation of the target object; inputting the rendered image, the first segmentation mask, an observed image of the target object, and a second segmentation mask of the target object in the observed image into a deep convolutional neural network to obtain a 6D pose estimation, a third segmentation mask and an optical flow; and performing said obtaining and said inputting again by updating the initial 6D pose estimation using the obtained relative 6D pose estimation and replacing the second segmentation mask with the third segmentation mask, to iteratively train the deep convolutional neural network.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.