In this paper, we deal with the problem of monocular depth estimation for fisheye cameras in a self-supervised manner. A known issue of self-supervised depth estimation is that it suffers in low-light/over-exposure conditions and in large homogeneous regions. To tackle this issue, we propose a novel ordinal distillation loss that distills the ordinal information from a large teacher model. Such a teacher model, since having been trained on a large amount of diverse data, can capture the depth ordering information well, but lacks in preserving accurate scene geometry. Combined with self-supervised losses, we show that our model can not only generate reasonable depth maps in challenging environments but also better recover the scene geometry. We further leverage the fisheye cameras of an AR-Glasses device to collect an indoor dataset to facilitate evaluation.

翻译：在本文中,我们以自我监督的方式处理对鱼眼照相机进行单眼深度估计的问题。一个已知的自我监督深度估计问题是,在低光/超暴露条件下以及在大片同一区域,它遭受到低光/超暴露条件和大片同一区域的影响。为解决这一问题,我们提议了一种新的园艺蒸馏损失,从一个大型教师模型中蒸馏出圆柱形信息。这种教师模型在经过大量不同数据的培训后,可以很好地捕捉到深度订购信息,但无法保存准确的现场几何。我们表明,我们的模型不仅能够在具有挑战性的环境中绘制合理的深度地图,而且能够更好地恢复现场的几何学。我们进一步利用一个AR-Glass装置的鱼眼照相机收集室内数据集,以便利评估。