Self-supervised monocular depth estimation networks are trained to predict
scene depth using nearby frames as a supervision signal during training.
However, for many applications, sequence information in the form of video
frames is also available at test time. The vast majority of monocular networks
do not make use of this extra signal, thus ignoring valuable information that
could be used to improve the predicted depth. Those that do, either use
computationally expensive test-time refinement techniques or off-the-shelf
recurrent networks, which only indirectly make use of the geometric information
that is inherently available.
We propose ManyDepth, an adaptive approach to dense depth estimation that can
make use of sequence information at test time, when it is available. Taking
inspiration from multi-view stereo, we propose a deep end-to-end cost volume
based approach that is trained using self-supervision only. We present a novel
consistency loss that encourages the network to ignore the cost volume when it
is deemed unreliable, e.g. in the case of moving objects, and an augmentation
scheme to cope with static cameras. Our detailed experiments on both KITTI and
Cityscapes show that we outperform all published self-supervised baselines,
including those that use single or multiple frames at test time.

提出 ManyDepth，一种自适应的、可以在测试时间利用序列信息的深度估计方法，通过深度自监督训练基于端到端代价体系积攒的方法，鼓励网络忽略不可靠的代价体以及针对静态相机场景的增强方案。实验证明此方法在 KITTI 和 Cityscapes 上表现优于其他单帧和多帧的自监督基线模型。