Self-Supervised Monocular Depth Underwater

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.

Authors

• Shlomi Amitai
• Itzik Klein
• Tali Treibitz

Keywords

• Training
• Automation
• Estimation
• Sensors
• Robots
• Changes In Appearance
• Depth Camera
• Depth Estimation
• Undersea
• Subsequent Frames
• Training Set
• Single Image
• Data Augmentation
• High-pass Filter
• Red Channel
• Path Planning
• Color Channels
• Illumination Changes
• Structure From Motion
• Camera Pose
• Autonomous Underwater Vehicles
• Reprojection Error
• Camera Orientation
• Influence Of Medium
• Background Error
• Ground Truth Depth
• Underwater Image
• Monocular Depth Estimation
• Non-uniform Illumination
• Training Frames
• RGB Images
• Sequence Of Frames
• Backscatter