Automatic evaluation of three-dimensional vehicle perception using two-dimensional deep neural networks
US12283112B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 4, 2022 |
| Grant date | Apr 22, 2025 |
| Priority date | — |
| Expiry date | Sep 8, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/647
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Vehicle perception techniques include applying a 3D DNN to a set of inputs to generate 3D detection results including a set of 3D objects, transforming the set of 3D objects onto a set of images as a first set of 2D bounding boxes, applying a 2D DNN to the set of images to generate 2D detection results including a second set of 2D bounding boxes, calculating mean average precision (mAP) values based on a comparison between the first and second sets of 2D bounding boxes, identifying a set or corner cases based on the calculated mAP values, and re-training or updating the 3D DNN using the identified set of corner cases, wherein a performance of the 3D DNN is thereby increased without the use of expensive additional manually and/or automatically annotated training datasets.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.