Methods and systems for semantic segmentation of a point cloud
US12205292B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 16, 2021 |
| Grant date | Jan 21, 2025 |
| Priority date | — |
| Expiry date | Oct 20, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems, methods and apparatus for sematic segmentation of 3D point clouds using deep neural networks. The deep neural network generally has two primary subsystems: a multi-branch cascaded subnetwork that includes an encoder and a decoder, and is configured to receive a sparse 3D point cloud, and capture and fuse spatial feature information in the sparse 3D point cloud at multiple scales and multi hierarchical levels; and a spatial feature transformer subnetwork that is configured to transform the cascaded features generated by the multi-branch cascaded subnetwork and fuse these scaled features using a shared decoder attention framework to assist in the prediction of sematic classes for the sparse 3D point cloud.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.