Self-supervised monocular depth estimation methods typically rely on the reprojection error to capture geometric relationships between successive frames in static environments. However, this assumption does not hold in dynamic objects in scenarios, leading to errors during the view synthesis stage, such as feature mismatch and occlusion, which can significantly reduce the accuracy of the generated depth maps. To address this problem, we propose a novel dynamic cost volume that exploits residual optical flow to describe moving objects, improving incorrectly occluded regions in static cost volumes used in previous work. Nevertheless, the dynamic cost volume inevitably generates extra occlusions and noise, thus we alleviate this by designing a fusion module that makes static and dynamic cost volumes compensate for each other. In other words, occlusion from the static volume is refined by the dynamic volume, and incorrect information from the dynamic volume is eliminated by the static volume. Furthermore, we propose a pyramid distillation loss to reduce photometric error inaccuracy at low resolutions and an adaptive photometric error loss to alleviate the flow direction of the large gradient in the occlusion regions. We conducted extensive experiments on the KITTI and Cityscapes datasets, and the results demonstrate that our model outperforms previously published baselines for self-supervised monocular depth estimation.

翻译：自监督单目深度估计方法通常依赖重投影误差捕捉静态环境中连续帧之间的几何关系。然而，在动态目标场景中，该假设不成立，会导致视角合成阶段出现特征失配和遮挡等错误，显著降低生成深度图的精度。为解决此问题，我们提出一种新颖的动态代价体积，利用残差光流描述运动物体，改进先前工作中静态代价体积内被错误遮挡的区域。但动态代价体积不可避免地会产生额外遮挡和噪声，为此我们设计融合模块，使静态与动态代价体积相互补偿。即，静态体积中的遮挡由动态体积修正，而动态体积中的错误信息则由静态体积消除。此外，我们提出金字塔蒸馏损失以降低低分辨率下光度误差的不准确性，并设计自适应光度误差损失以缓解遮挡区域中大梯度的光流方向。在KITTI和Cityscapes数据集上的大量实验表明，我们的模型在自监督单目深度估计任务中优于先前已发表的基线方法。