  Estimating depth from a single image is a challenging visual task. Compared
to relative depth estimation, metric depth estimation attracts more attention
due to its practical physical significance and critical applications in
real-life scenarios. However, existing metric depth estimation methods are
typically trained on specific datasets with similar scenes, facing challenges
in generalizing across scenes with significant scale variations. To address
this challenge, we propose a novel monocular depth estimation method called
ScaleDepth. Our method decomposes metric depth into scene scale and relative
depth, and predicts them through a semantic-aware scale prediction (SASP)
module and an adaptive relative depth estimation (ARDE) module, respectively.
The proposed ScaleDepth enjoys several merits. First, the SASP module can
implicitly combine structural and semantic features of the images to predict
precise scene scales. Second, the ARDE module can adaptively estimate the
relative depth distribution of each image within a normalized depth space.
Third, our method achieves metric depth estimation for both indoor and outdoor
scenes in a unified framework, without the need for setting the depth range or
fine-tuning model. Extensive experiments demonstrate that our method attains
state-of-the-art performance across indoor, outdoor, unconstrained, and unseen
scenes. Project page: https://ruijiezhu94.github.io/ScaleDepth
