  Learning-based monocular depth estimation leverages geometric priors present
in the training data to enable metric depth perception from a single image, a
traditionally ill-posed problem. However, these priors are often specific to a
particular domain, leading to limited generalization performance on unseen
data. Apart from the well studied environmental domain gap, monocular depth
estimation is also sensitive to the domain gap induced by varying camera
parameters, an aspect that is often overlooked in current state-of-the-art
approaches. This issue is particularly evident in autonomous driving scenarios,
where datasets are typically collected with a single vehicle-camera setup,
leading to a bias in the training data due to a fixed perspective geometry. In
this paper, we challenge this trend and introduce GenDepth, a novel model
capable of performing metric depth estimation for arbitrary vehicle-camera
setups. To address the lack of data with sufficiently diverse camera
parameters, we first create a bespoke synthetic dataset collected with
different vehicle-camera systems. Then, we design GenDepth to simultaneously
optimize two objectives: (i) equivariance to the camera parameter variations on
synthetic data, (ii) transferring the learned equivariance to real-world
environmental features using a single real-world dataset with a fixed
vehicle-camera system. To achieve this, we propose a novel embedding of camera
parameters as the ground plane depth and present a novel architecture that
integrates these embeddings with adversarial domain alignment. We validate
GenDepth on several autonomous driving datasets, demonstrating its
state-of-the-art generalization capability for different vehicle-camera
systems.
