Generating depth images utilizing a machine-learning model built from mixed digital image sources and multiple loss function sets
US11798180B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 26, 2021 |
| Grant date | Oct 24, 2023 |
| Priority date | — |
| Expiry date | Sep 23, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
This disclosure describes one or more implementations of a depth prediction system that generates accurate depth images from single input digital images. In one or more implementations, the depth prediction system enforces different sets of loss functions across mix-data sources to generate a multi-branch architecture depth prediction model. For instance, in one or more implementations, the depth prediction model utilizes different data sources having different granularities of ground truth depth data to robustly train a depth prediction model. Further, given the different ground truth depth data granularities from the different data sources, the depth prediction model enforces different combinations of loss functions including an image-level normalized regression loss function and/or a pair-wise normal loss among other loss functions.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.