  Computer vision methods for depth estimation usually use simple camera models
with idealized optics. For modern machine learning approaches, this creates an
issue when attempting to train deep networks with simulated data, especially
for focus-sensitive tasks like Depth-from-Focus. In this work, we investigate
the domain gap caused by off-axis aberrations that will affect the decision of
the best-focused frame in a focal stack. We then explore bridging this domain
gap through aberration-aware training (AAT). Our approach involves a
lightweight network that models lens aberrations at different positions and
focus distances, which is then integrated into the conventional network
training pipeline. We evaluate the generality of pretrained models on both
synthetic and real-world data. Our experimental results demonstrate that the
proposed AAT scheme can improve depth estimation accuracy without fine-tuning
the model or modifying the network architecture.
