Learning rigidity of dynamic scenes for three-dimensional scene flow estimation
US10929987B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 1, 2018 |
| Grant date | Feb 23, 2021 |
| Priority date | — |
| Expiry date | Sep 3, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A neural network model receives color data for a sequence of images corresponding to a dynamic scene in three-dimensional (3D) space. Motion of objects in the image sequence results from a combination of a dynamic camera orientation and motion or a change in the shape of an object in the 3D space. The neural network model generates two components that are used to produce a 3D motion field representing the dynamic (non-rigid) part of the scene. The two components are information identifying dynamic and static portions of each image and the camera orientation. The dynamic portions of each image contain motion in the 3D space that is independent of the camera orientation. In other words, the motion in the 3D space (estimated 3D scene flow data) is separated from the motion of the camera.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.