Depth data model training with upsampling, losses and loss balancing
US11681046B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 22, 2021 |
| Grant date | Jun 20, 2023 |
| Priority date | — |
| Expiry date | Dec 31, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/07
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques for training a machine learned (ML) model to determine depth data based on image data are discussed herein. Training can use stereo image data and depth data (e.g., lidar data). A first (e.g., left) image can be input to a ML model, which can output predicted disparity and/or depth data. The predicted disparity data can be used with second image data (e.g., a right image) to reconstruct the first image. Differences between the first and reconstructed images can be used to determine a loss. Losses may include pixel, smoothing, structural similarity, and/or consistency losses. Further, differences between the depth data and the predicted depth data and/or differences between the predicted disparity data and the predicted depth data can be determined, and the ML model can be trained based on the various losses. Thus, the techniques can use self-supervised training and supervised training to train a ML model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.