Self-supervised training of a depth estimation model using depth hints
US11711508B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 16, 2022 |
| Grant date | Jul 25, 2023 |
| Priority date | — |
| Expiry date | Mar 16, 2042 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04N2013/0081
- WIPO fieldAudio-visual technology
- WIPO sectorElectrical engineering
Abstract
A method for training a depth estimation model with depth hints is disclosed. For each image pair: for a first image, a depth prediction is determined by the depth estimation model and a depth hint is obtained; the second image is projected onto the first image once to generate a synthetic frame based on the depth prediction and again to generate a hinted synthetic frame based on the depth hint; a primary loss is calculated with the synthetic frame; a hinted loss is calculated with the hinted synthetic frame; and an overall loss is calculated for the image pair based on a per-pixel determination of whether the primary loss or the hinted loss is smaller, wherein if the hinted loss is smaller than the primary loss, then the overall loss includes the primary loss and a supervised depth loss between depth prediction and depth hint. The depth estimation model is trained by minimizing the overall losses for the image pairs.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.