Representation learning using machine learning classification tasks based on point clouds of interacting 3D surfaces
US11532171B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 2, 2020 |
| Grant date | Dec 20, 2022 |
| Priority date | — |
| Expiry date | Jan 6, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/82
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method and apparatus for determining spatial characteristics of three-dimensional objects is described. In an exemplary embodiment, the device receives a point cloud representation of a three-dimensional surface structure of a plurality of objects. The device may further generate a set of bins to represent the three-dimensional surface structure based on the point cloud representation, each bin corresponding to a spatial occupancy related to the point cloud representation, each bin including a respective type indicating a spatial relationship of the surface structures and a corresponding spatial occupancy of the bin. In addition, the device may encode the set of bins using a convolutional neural network. The device may further determine a classification for the spatial characteristic of the surface structures based on the convolutional neural network with the encoded set of bins.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.