Self-supervised monocular depth estimation has shown impressive results in static scenes. It relies on the multi-view consistency assumption for training networks, however, that is violated in dynamic object regions and occlusions. Consequently, existing methods show poor accuracy in dynamic scenes, and the estimated depth map is blurred at object boundaries because they are usually occluded in other training views. In this paper, we propose SC-DepthV3 for addressing the challenges. Specifically, we introduce an external pretrained monocular depth estimation model for generating single-image depth prior, namely pseudo-depth, based on which we propose novel losses to boost self-supervised training. As a result, our model can predict sharp and accurate depth maps, even when training from monocular videos of highly-dynamic scenes. We demonstrate the significantly superior performance of our method over previous methods on six challenging datasets, and we provide detailed ablation studies for the proposed terms. Source code and data will be released at https://github.com/JiawangBian/sc_depth_pl

翻译：自监督单目深度估计在静态场景中已展现出显著成果。该方法依赖多视角一致性假设来训练网络，然而该假设在动态物体区域和遮挡区域中并不成立。因此，现有方法在动态场景中精度较差，且由于目标边界通常在其他训练视角中被遮挡，估计的深度图在物体边缘处模糊不清。本文提出SC-DepthV3以应对上述挑战。具体而言，我们引入外部预训练的单目深度估计模型生成单图像深度先验（即伪深度），并基于此提出新型损失函数以增强自监督训练。由此，即使从高度动态场景的单目视频中进行训练，我们的模型也能预测出锐利且精准的深度图。我们在六个具有挑战性的数据集上证明本方法相较先前方法具有显著优越性能，并针对所提各项损失项进行了详细的消融研究。源代码与数据将发布于https://github.com/JiawangBian/sc_depth_pl。