Deep-learning method for separating reflection and transmission images visible at a semi-reflective surface in a computer image of a real-world scene
US11270161B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 8, 2020 |
| Grant date | Mar 8, 2022 |
| Priority date | — |
| Expiry date | Sep 18, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/955
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
When a computer image is generated from a real-world scene having a semi-reflective surface (e.g. window), the computer image will create, at the semi-reflective surface from the viewpoint of the camera, both a reflection of a scene in front of the semi-reflective surface and a transmission of a scene located behind the semi-reflective surface. Similar to a person viewing the real-world scene from different locations, angles, etc., the reflection and transmission may change, and also move relative to each other, as the viewpoint of the camera changes. Unfortunately, the dynamic nature of the reflection and transmission negatively impacts the performance of many computer applications, but performance can generally be improved if the reflection and transmission are separated. The present disclosure uses deep learning to separate reflection and transmission at a semi-reflective surface of a computer image generated from a real-world scene.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.