High resolution 3D point clouds generation based on CNN and CRF models
US10671082B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 3, 2017 |
| Grant date | Jun 2, 2020 |
| Priority date | — |
| Expiry date | Feb 15, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30252
- WIPO fieldAudio-visual technology
- WIPO sectorElectrical engineering
Abstract
In one embodiment, a method or system generates a high resolution 3-D point cloud to operate an autonomous driving vehicle (ADV) from a low resolution 3-D point cloud and camera-captured image(s). The system receives a first image captured by a camera for a driving environment. The system receives a second image representing a first depth map of a first point cloud corresponding to the driving environment. The system determines a second depth map by applying a convolutional neural network model to the first image. The system generates a third depth map by applying a conditional random fields model to the first image, the second image and the second depth map, the third depth map having a higher resolution than the first depth map such that the third depth map represents a second point cloud perceiving the driving environment surrounding the ADV.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.