Grouped mathematical differentiable NMS for object detection
US12354383B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 22, 2022 |
| Grant date | Jul 8, 2025 |
| Priority date | — |
| Expiry date | Apr 25, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/82
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An end-to-end trainable grouped mathematically differentiable non-maximal suppression (NMS) technique is presented for monocular 3D object detection. First, formulate NMS as a matrix operation and then group and mask the boxes in an unsupervised manner to obtain a simple closed-form expression of the NMS. This technique addresses the mismatch between training and inference pipelines and, therefore, forces the network to select the best 3D box in a differentiable manner. As a result, the proposed technique achieves state-of-the-art monocular 3D object detection results on the KITTI benchmark dataset performing comparably to monocular video-based methods.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.