Stereo depth estimation using deep neural networks
US11080590B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 18, 2019 |
| Grant date | Aug 3, 2021 |
| Priority date | — |
| Expiry date | Aug 21, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
Various examples of the present disclosure include a stereoscopic deep neural network (DNN) that produces accurate and reliable results in real-time. Both LIDAR data (supervised training) and photometric error (unsupervised training) may be used to train the DNN in a semi-supervised manner. The stereoscopic DNN may use an exponential linear unit (ELU) activation function to increase processing speeds, as well as a machine learned argmax function that may include a plurality of convolutional layers having trainable parameters to account for context. The stereoscopic DNN may further include layers having an encoder/decoder architecture, where the encoder portion of the layers may include a combination of three-dimensional convolutional layers followed by two-dimensional convolutional layers.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.