Significant attention has been attracted to deep learning-based depth
estimates. Dynamic objects become the most hard problems in
inter-frame-supervised depth estimates due to the uncertainty in adjacent
frames. Thus, integrating optical flow information with depth estimation is a
feasible solution, as the optical flow is an essential motion representation.
In this work, we construct a joint inter-frame-supervised depth and optical
flow estimation framework, which predicts depths in various motions by
minimizing pixel wrap errors in bilateral photometric re-projections and
optical vectors. For motion segmentation, we adaptively segment the preliminary
estimated optical flow map with large areas of connectivity. In self-supervised
depth estimation, different motion regions are predicted independently and then
composite into a complete depth. Further, the pose and depth estimations
re-synthesize the optical flow maps, serving to compute reconstruction errors
with the preliminary predictions. Our proposed joint depth and optical flow
estimation outperforms existing depth estimators on the KITTI Depth dataset,
both with and without Cityscapes pretraining. Additionally, our optical flow
results demonstrate competitive performance on the KITTI Flow 2015 dataset.

本研究提出了一种基于深度学习的深度估计方法，通过将光流信息与深度估计相结合，在预测不同运动情况下的深度时减小像素包裹误差和光学向量，利用自我监督深度估计方法对不同运动区域进行独立估计并组合为完整的深度，能够比 KITTI Depth 数据集上的现有深度估计器表现更好，同时在 KITTI Flow 2015 数据集上也达到了竞争性的光流性能。