Self-supervised monocular depth estimation (MDE) models universally suffer
from the notorious edge-fattening issue. Triplet loss, as a widespread metric
learning strategy, has largely succeeded in many computer vision applications.
In this paper, we redesign the patch-based triplet loss in MDE to alleviate the
ubiquitous edge-fattening issue. We show two drawbacks of the raw triplet loss
in MDE and demonstrate our problem-driven redesigns. First, we present a min.
operator based strategy applied to all negative samples, to prevent
well-performing negatives sheltering the error of edge-fattening negatives.
Second, we split the anchor-positive distance and anchor-negative distance from
within the original triplet, which directly optimizes the positives without any
mutual effect with the negatives. Extensive experiments show the combination of
these two small redesigns can achieve unprecedented results: Our powerful and
versatile triplet loss not only makes our model outperform all previous SoTA by
a large margin, but also provides substantial performance boosts to a large
number of existing models, while introducing no extra inference computation at
all.

本论文通过重新设计面向自监督单目深度估计（MDE）模型的基于贴片的三元组损失以减轻普遍存在的边缘膨胀问题，并利用两种新策略，分别基于所有负样本进行 min. 操作和在原始三元组中将锚点 - 正样本距离和锚点 - 负样本距离分离，取得了前所未有的成果，使本模型在性能方面大幅优于所有之前的 SOTA，同时也为大量现有模型带来了实质性的性能提升，而且不需额外的推理计算。