  Monocular depth estimation has been widely studied, and significant
improvements in performance have been recently reported. However, most previous
works are evaluated on a few benchmark datasets, such as KITTI datasets, and
none of the works provide an in-depth analysis of the generalization
performance of monocular depth estimation. In this paper, we deeply investigate
the various backbone networks (e.g.CNN and Transformer models) toward the
generalization of monocular depth estimation. First, we evaluate
state-of-the-art models on both in-distribution and out-of-distribution
datasets, which have never been seen during network training. Then, we
investigate the internal properties of the representations from the
intermediate layers of CNN-/Transformer-based models using synthetic
texture-shifted datasets. Through extensive experiments, we observe that the
Transformers exhibit a strong shape-bias rather than CNNs, which have a strong
texture-bias. We also discover that texture-biased models exhibit worse
generalization performance for monocular depth estimation than shape-biased
models. We demonstrate that similar aspects are observed in real-world driving
datasets captured under diverse environments. Lastly, we conduct a dense
ablation study with various backbone networks which are utilized in modern
strategies. The experiments demonstrate that the intrinsic locality of the CNNs
and the self-attention of the Transformers induce texture-bias and shape-bias,
respectively.
