Method and system for deep neural networks using dynamically selected feature-relevant points from a point cloud
US11676005B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 14, 2018 |
| Grant date | Jun 13, 2023 |
| Priority date | — |
| Expiry date | Jun 14, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/64
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and systems for deep neural networks using dynamically selected feature-relevant points from a point cloud are described. A plurality of multidimensional feature vectors arranged in a point-feature matrix are received. Each row of the point-feature matrix corresponds to a respective one of the multidimensional feature vectors, and each column of the point-feature matrix corresponds to a respective feature. Each multidimensional feature vector represents a respective unordered point from a point cloud and each multidimensional feature vector includes a respective plurality of feature-correlated values, each feature-correlated value represents a correlation extent of the respective feature. A reduced-max matrix having a selected plurality of feature-relevant vectors is generated. The feature-relevant vectors are selected by, for each respective feature, identifying a respective multidimensional feature vector in the point-feature matrix having a maximum feature-correlated value associated with the respective feature. The reduced-max matrix is output to at least one neural network layer.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.