Monocular depth estimators can be trained with various forms of
self-supervision from binocular-stereo data to circumvent the need for
high-quality laser scans or other ground-truth data. The disadvantage, however,
is that the photometric reprojection losses used with self-supervised learning
typically have multiple local minima. These plausible-looking alternatives to
ground truth can restrict what a regression network learns, causing it to
predict depth maps of limited quality. As one prominent example, depth
discontinuities around thin structures are often incorrectly estimated by
current state-of-the-art methods.
Here, we study the problem of ambiguous reprojections in depth prediction
from stereo-based self-supervision, and introduce Depth Hints to alleviate
their effects. Depth Hints are complementary depth suggestions obtained from
simple off-the-shelf stereo algorithms. These hints enhance an existing
photometric loss function, and are used to guide a network to learn better
weights. They require no additional data, and are assumed to be right only
sometimes. We show that using our Depth Hints gives a substantial boost when
training several leading self-supervised-from-stereo models, not just our own.
Further, combined with other good practices, we produce state-of-the-art depth
predictions on the KITTI benchmark.

该研究针对单目深度估计的自监督学习方法，在不需要昂贵激光扫描或其他基础数据的情况下，使用 Depth Hints 通过加入来自简单立体算法的参考感知补充了光度学重投影代价函数，从而提高训练深度估计网络时的深度预测精度，该方法取得了在 KITTI 数据集上的最优预测效果。