  Monocular camera calibration is a key precondition for numerous 3D vision
applications. Despite considerable advancements, existing methods often hinge
on specific assumptions and struggle to generalize across varied real-world
scenarios, and the performance is limited by insufficient training data.
Recently, diffusion models trained on expansive datasets have been confirmed to
maintain the capability to generate diverse, high-quality images. This success
suggests a strong potential of the models to effectively understand varied
visual information. In this work, we leverage the comprehensive visual
knowledge embedded in pre-trained diffusion models to enable more robust and
accurate monocular camera intrinsic estimation. Specifically, we reformulate
the problem of estimating the four degrees of freedom (4-DoF) of camera
intrinsic parameters as a dense incident map generation task. The map details
the angle of incidence for each pixel in the RGB image, and its format aligns
well with the paradigm of diffusion models. The camera intrinsic then can be
derived from the incident map with a simple non-learning RANSAC algorithm
during inference. Moreover, to further enhance the performance, we jointly
estimate a depth map to provide extra geometric information for the incident
map estimation. Extensive experiments on multiple testing datasets demonstrate
that our model achieves state-of-the-art performance, gaining up to a 40%
reduction in prediction errors. Besides, the experiments also show that the
precise camera intrinsic and depth maps estimated by our pipeline can greatly
benefit practical applications such as 3D reconstruction from a single
in-the-wild image.
