Name
Title | ||
|---|---|---|
Self-Supervised Learning for Monocular Depth Estimation on Minimally Invasive Surgery Scenes |
Abstract | ||
|---|---|---|
Self-supervised learning algorithms that compute depth map from monocular videos have achieved remarkable performance on urban scenes and have been applied extensively. These techniques still face significant challenges, however, when applied directly to endoscopic videos because of the brightness variations from frame to frame and inadequate representation learning during the training phase. Inspired by the optical flow for motion alignment between adjacent frames, we design a AFNet with structural stability loss and residual-based smoothness loss to learn the appearance flow across adjacent frames, which handles the brightness inconsistency issue efficaciously. In addition, we propose a novel self-attention mechanism named feature scaling module to alleviate the inadequate representation learning problem. In a comparison study to the current state-of-the-art self-supervised methods explored for urban videos on the SCARED dataset, the developed model surpasses existing methods by a large margin. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICRA48506.2021.9561508 | 2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021) |
DocType | Volume | Issue |
Conference | 2021 | 1 |
ISSN | Citations | PageRank |
1050-4729 | 0 | 0.34 |
References | Authors | |
6 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuwei Shao | 1 | 2 | 1.05 |
| Zhongcai Pei | 2 | 6 | 4.88 |
| Chen Weihai | 3 | 190 | 38.21 |
| Baochang Zhang | 4 | 1130 | 93.76 |
| Xingming Wu | 5 | 7 | 2.77 |
| Dianmin Sun | 6 | 3 | 3.09 |
| David Doermann | 7 | 4313 | 312.70 |