The field of self-supervised monocular depth estimation has seen huge
advancements in recent years. Most methods assume stereo data is available
during training but usually under-utilize it and only treat it as a reference
signal. We propose a novel self-supervised approach which uses both left and
right images equally during training, but can still be used with a single input
image at test time, for monocular depth estimation. Our Siamese network
architecture consists of two, twin networks, each learns to predict a disparity
map from a single image. At test time, however, only one of these networks is
used in order to infer depth. We show state-of-the-art results on the standard
KITTI Eigen split benchmark as well as being the highest scoring
self-supervised method on the new KITTI single view benchmark. To demonstrate
the ability of our method to generalize to new data sets, we further provide
results on the Make3D benchmark, which was not used during training.

本论文提出了一种新型的自监督单目深度估计方法，使用两张图像进行训练以及基于 Siamese 网络结构只使用其中一张图像来预测深度，在 KITTI 基准测试和 Make3D 基准测试中均取得了最优结果。