We present two versatile methods to generally enhance self-supervised
monocular depth estimation (MDE) models. The high generalizability of our
methods is achieved by solving the fundamental and ubiquitous problems in
photometric loss function. In particular, from the perspective of spatial
frequency, we first propose Ambiguity-Masking to suppress the incorrect
supervision under photometric loss at specific object boundaries, the cause of
which could be traced to pixel-level ambiguity. Second, we present a novel
frequency-adaptive Gaussian low-pass filter, designed to robustify the
photometric loss in high-frequency regions. We are the first to propose
blurring images to improve depth estimators with an interpretable analysis.
Both modules are lightweight, adding no parameters and no need to manually
change the network structures. Experiments show that our methods provide
performance boosts to a large number of existing models, including those who
claimed state-of-the-art, while introducing no extra inference computation at
all.

提出了两种通用方法来增强自监督单目深度估计模型的普适性，分别是模糊边界和自适应高斯滤波器。实验证明这两个模块能为现有模型带来性能提升而不增加额外计算量。