Machine-trained network for misalignment-insensitive depth perception
US10453220B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 12, 2018 |
| Grant date | Oct 22, 2019 |
| Priority date | — |
| Expiry date | Jan 12, 2038 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04N2013/0081
- WIPO fieldAudio-visual technology
- WIPO sectorElectrical engineering
Abstract
Some embodiments of the invention provide a novel method for training a multi-layer node network to reliably determine depth based on a plurality of input sources (e.g., cameras, microphones, etc.) that may be arranged with deviations from an ideal alignment or placement. Some embodiments train the multi-layer network using a set of inputs generated with random misalignments incorporated into the training set. In some embodiments, the training set includes (i) a synthetically generated training set based on a three-dimensional ground truth model as it would be sensed by a sensor array from different positions and with different deviations from ideal alignment and placement, and/or (ii) a training set generated by a set of actual sensor arrays augmented with an additional sensor (e.g., additional camera or time of flight measurement device such as lidar) to collect ground truth data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.