Depth estimation is critical for any robotic system. In the past years, the estimation of depth from monocular images has shown great improvement. However, in the underwater environment results are still lagging behind due to appearance changes caused by the medium. So far little effort has been invested in overcoming this. Moreover, underwater, there are more limitations to using high-resolution depth sensors, which is a serious obstacle to generating ground truth. So far unsupervised methods that tried to solve this have achieved limited success as they relied on domain transfer from a dataset in the air. We suggest network training using subsequent frames, self-supervised by a reprojection loss, as was demonstrated successfully above water. We propose several additions to the self-supervised framework to cope with the underwater environment and achieve state-of-the-art results on a challenging forward-looking underwater dataset.
|Title of host publication||Proceedings - ICRA 2023|
|Subtitle of host publication||IEEE International Conference on Robotics and Automation|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||7|
|State||Published - 2023|
|Event||2023 IEEE International Conference on Robotics and Automation, ICRA 2023 - London, United Kingdom|
Duration: 29 May 2023 → 2 Jun 2023
|Name||Proceedings - IEEE International Conference on Robotics and Automation|
|Conference||2023 IEEE International Conference on Robotics and Automation, ICRA 2023|
|Period||29/05/23 → 2/06/23|
Bibliographical noteFunding Information:
The research was funded by Israel Science Foundation grant #680/18, the Israeli Ministry of Science and Technology grant #3 − 15621, the Israel Data Science Initiative (IDSI) of the Council for Higher Education in Israel, the Data Science Research Center at the University of Haifa, and the European Union’s Horizon 2020 research and innovation program under grant agreement No. GA 101016958.
© 2023 IEEE.
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Artificial Intelligence