论文标题
自我监督的单眼深度在水下
Self-Supervised Monocular Depth Underwater
论文作者
论文摘要
深度估计对于任何机器人系统至关重要。在过去的几年中，单眼图像的深度估计显示出了很大的改善，但是，由于介质引起的外观变化，在水下环境中的结果仍落后。到目前为止，几乎没有为克服这一点而投入的努力。此外，在水下，使用高分辨率深度传感器存在更多的局限性，这使得为学习方法生成了另一个巨大障碍。到目前为止，试图解决这一问题的无监督方法取得了非常有限的成功，因为它们依赖于空气中数据集的域传输。我们建议使用随后的帧进行训练，因为再投入损失自我监督，这是在水上成功证明的。我们建议对自我监督的框架进行一些补充，以应对水下环境，并在充满挑战的前瞻性水下数据集中获得最新的结果。
Depth estimation is critical for any robotic system. In the past years estimation of depth from monocular images have 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 on overcoming this. Moreover, underwater, there are more limitations for using high resolution depth sensors, this makes generating ground truth for learning methods another enormous obstacle. So far unsupervised methods that tried to solve this have achieved very limited success as they relied on domain transfer from dataset in air. We suggest training using subsequent frames self-supervised by a reprojection loss, as was demonstrated successfully above water. We suggest 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.
