Name
Title | ||
|---|---|---|
Deep hierarchical guidance and regularization learning for end-to-end depth estimation. |
Abstract | ||
|---|---|---|
•We propose a Hierarchical Guidance and Regularization (HGR) learning framework for end-to-end monocular depth estimation.•A multi-regularized learning strategy is to optimize network parameters by employing multi-level information of depth maps.•The proposed method obtains state-of-the-art depth estimation performance on NYU Depth V2, KITTI and Make3D datasets. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.patcog.2018.05.016 | Pattern Recognition |
Keywords | Field | DocType |
Depth estimation,Multi-regularization,Deep neural network | Pattern recognition,End-to-end principle,Ground truth,Regularization (mathematics),Artificial intelligence,Monocular,Upsampling,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
83 | 1 | 0031-3203 |
Citations | PageRank | References |
6 | 0.46 | 34 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenyu Zhang | 1 | 30 | 7.19 |
| Chunyan Xu | 2 | 169 | 18.10 |
| Jian Yang | 3 | 6102 | 339.77 |
| Ying Tai | 4 | 213 | 25.74 |
| Liang Chen | 5 | 313 | 36.77 |