  Indoor monocular depth estimation has attracted increasing research interest.
Most previous works have been focusing on methodology, primarily experimenting
with NYU-Depth-V2 (NYUv2) Dataset, and only concentrated on the overall
performance over the test set. However, little is known regarding robustness
and generalization when it comes to applying monocular depth estimation methods
to real-world scenarios where highly varying and diverse functional
\textit{space types} are present such as library or kitchen. A study for
performance breakdown into space types is essential to realize a pretrained
model's performance variance. To facilitate our investigation for robustness
and address limitations of previous works, we collect InSpaceType, a
high-quality and high-resolution RGBD dataset for general indoor environments.
We benchmark 12 recent methods on InSpaceType and find they severely suffer
from performance imbalance concerning space types, which reveals their
underlying bias. We extend our analysis to 4 other datasets, 3 mitigation
approaches, and the ability to generalize to unseen space types. Our work marks
the first in-depth investigation of performance imbalance across space types
for indoor monocular depth estimation, drawing attention to potential safety
concerns for model deployment without considering space types, and further
shedding light on potential ways to improve robustness. See
\url{https://depthcomputation.github.io/DepthPublic} for data and the
supplementary document. The benchmark list on the GitHub project page keeps
updates for the lastest monocular depth estimation methods.
