Learning neural light fields with ray-space embedding networks
US12327308B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 1, 2022 |
| Grant date | Jun 10, 2025 |
| Priority date | — |
| Expiry date | Apr 24, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In particular embodiments, a computing system may access a set of training images for training a neural light field network for view synthesis. Using each training image, the computing system may train the neural light field network by casting, for each pixel of the training image, a ray into a three-dimensional (3D) space, the ray including integrated radiance along the ray, mapping first ray coordinates of the ray into an embedding network, transforming, using the embedding network, the first ray coordinates into second ray coordinates, applying positional encoding to second ray coordinates, generating, using the neural light field network, a predicted color value for the pixel based on positionally encoded second ray coordinates, comparing the predicted color value for the pixel with a ground-truth color value for the pixel, and updating the neural light field network and the embedding network based on comparison.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.