  Deep networks for Monocular Depth Estimation (MDE) have achieved promising
performance recently and it is of great importance to further understand the
interpretability of these networks. Existing methods attempt to provide posthoc
explanations by investigating visual cues, which may not explore the internal
representations learned by deep networks. In this paper, we find that some
hidden units of the network are selective to certain ranges of depth, and thus
such behavior can be served as a way to interpret the internal representations.
Based on our observations, we quantify the interpretability of a deep MDE
network by the depth selectivity of its hidden units. Moreover, we then propose
a method to train interpretable MDE deep networks without changing their
original architectures, by assigning a depth range for each unit to select.
Experimental results demonstrate that our method is able to enhance the
interpretability of deep MDE networks by largely improving the depth
selectivity of their units, while not harming or even improving the depth
estimation accuracy. We further provide a comprehensive analysis to show the
reliability of selective units, the applicability of our method on different
layers, models, and datasets, and a demonstration on analysis of model error.
Source code and models are available at
https://github.com/youzunzhi/InterpretableMDE .
