Self-supervised monocular depth estimation has received much attention recently in computer vision. Most of the existing works in literature aggregate multi-scale features for depth prediction via either straightforward concatenation or element-wise addition, however, such feature aggregation operations generally neglect the contextual consistency between multi-scale features. Addressing this problem, we propose the Self-Distilled Feature Aggregation (SDFA) module for simultaneously aggregating a pair of low-scale and high-scale features and maintaining their contextual consistency. The SDFA employs three branches to learn three feature offset maps respectively: one offset map for refining the input low-scale feature and the other two for refining the input highscale feature under a designed self-distillation manner. Then, we propose an SDFA-based network for self-supervised monocular depth estimation, and design a self-distilled training strategy to train the proposed network with the SDFA module. Experimental results on the KITTI dataset demonstrate that the proposed method outperforms the comparative state-of-the-art methods in most cases.
Results on KITTI raw test set
| Method | Info. | Train. Data | Sup | PP | Abs Rel. | Sq Rel. | RMSE | RMSElog | A1 | Model |
|---|---|---|---|---|---|---|---|---|---|---|
| SDFA-Net | SwinT*+384x1280 | K | Stereo | 0.089 | 0.537 | 3.895 | 0.169 | 0.906 | Baidu/Google | |
| SDFA-Net | SwinT*+384x1280 | K | Stereo | pp | 0.088 | 0.530 | 3.864 | 0.168 | 0.907 | Baidu/Google |
| SDFA-Net | SwinT*+384x1280 | CS+K | Stereo | 0.084 | 0.528 | 3.887 | 0.167 | 0.911 | Baidu/Google | |
| SDFA-Net | SwinT*+384x1280 | CS+K | Stereo | pp | 0.084 | 0.521 | 3.832 | 0.166 | 0.913 | Baidu/Google |
Results on KITTI improved test set
| Method | Info. | Train. Data | Sup | PP | Abs Rel. | Sq Rel. | RMSE | RMSElog | A1 | Model |
|---|---|---|---|---|---|---|---|---|---|---|
| SDFA-Net | SwinT*+384x1280 | K | Stereo | 0.073 | 0.231 | 2.581 | 0.101 | 0.955 | Baidu/Google | |
| SDFA-Net | SwinT*+384x1280 | K | Stereo | pp | 0.073 | 0.227 | 2.545 | 0.101 | 0.956 | Baidu/Google |
| SDFA-Net | SwinT*+384x1280 | CS+K | Stereo | 0.069 | 0.214 | 2.542 | 0.096 | 0.962 | Baidu/Google | |
| SDFA-Net | SwinT*+384x1280 | CS+K | Stereo | pp | 0.068 | 0.206 | 2.471 | 0.095 | 0.963 | Baidu/Google |
- code for all the download links of pan Baidu is
smde.