Robust Monocular Depth Estimation under Challenging Conditions
Stefano Gasperini Nils Morbitzer HyunJun Jung Nassir Navab Federico Tombari
While state-of-the-art monocular depth estimation approaches achieve
impressive results in ideal settings, they are highly unreliable under
challenging illumination and weather conditions, such as at nighttime or in the
presence of rain. In this paper, we uncover these safety-critical issues and
tackle them with md4all: a simple and effective solution that works reliably
under both adverse and ideal conditions, as well as for different types of
learning supervision. We achieve this by exploiting the efficacy of existing
methods under perfect settings. Therefore, we provide valid training signals
independently of what is in the input. First, we generate a set of complex
samples corresponding to the normal training ones. Then, we train the model by
guiding its self- or full-supervision by feeding the generated samples and
computing the standard losses on the corresponding original images. Doing so
enables a single model to recover information across diverse conditions without
modifications at inference time. Extensive experiments on two challenging
public datasets, namely nuScenes and Oxford RobotCar, demonstrate the
effectiveness of our techniques, outperforming prior works by a large margin in
both standard and challenging conditions. Source code and data are available
at: https://md4all.github.io.