Rendering images from deeply learned raytracing parameters
US10902665B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 28, 2019 |
| Grant date | Jan 26, 2021 |
| Priority date | — |
| Expiry date | Apr 2, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/048
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Images are rendered from deeply learned raytracing parameters. Active learning, via a machine learning (ML) model (e.g., implemented by a deep neural network), is used to automatically determine, infer, and/or predict optimized, or at least somewhat optimized, values for parameters used in raytracing methods. Utilizing deep learning to determine optimized, or at least somewhat optimized, values for raytracing parameters is in contrast to conventional methods, which require users to rely of heuristics for parameter value setting. In various embodiments, one or more parameters regarding the termination and splitting of traced light paths in stochastic-based (e.g., Monte Carlo) raytracing are determined via active learning. In some embodiments, one or more parameters regarding the sampling rate of shadow rays are also determined.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.