Systems and methods for depth estimation using convolutional spatial propagation networks
US10839543B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 26, 2019 |
| Grant date | Nov 17, 2020 |
| Priority date | — |
| Expiry date | Mar 24, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Presented are systems and methods for improving speed and quality of real-time per-pixel depth estimation of scene layouts from a single image by using a 3D end-to-end Convolutional Spatial Propagation Network (CSPN). An efficient linear propagation model performs propagation using a recurrent convolutional operation. The affinity among neighboring pixels may be learned through a deep convolutional neural network (CNN). The CSPN may be applied to two depth estimation tasks, given a single image: (1) to refine the depth output of existing methods, and (2) to convert sparse depth samples to a dense depth map, e.g., by embedding the depth samples within the propagation procedure. For stereo depth estimation, the 3D CPSN is applied to stereo matching by adding a diffusion dimension over discrete disparity space and feature scale space. This aids the recovered stereo depth to generate more details and to avoid error matching from noisy appearance caused by sunlight, shadow, and similar effects.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.