  3D sensing for monocular in-the-wild images, e.g., depth estimation and 3D
object detection, has become increasingly important. However, the unknown
intrinsic parameter hinders their development and deployment. Previous methods
for the monocular camera calibration rely on specific 3D objects or strong
geometry prior, such as using a checkerboard or imposing a Manhattan World
assumption. This work solves the problem from the other perspective by
exploiting the monocular 3D prior. Our method is assumption-free and calibrates
the complete $4$ Degree-of-Freedom (DoF) intrinsic parameters. First, we
demonstrate intrinsic is solved from two well-studied monocular priors, i.e.,
monocular depthmap, and surface normal map. However, this solution imposes a
low-bias and low-variance requirement for depth estimation. Alternatively, we
introduce a novel monocular 3D prior, the incidence field, defined as the
incidence rays between points in 3D space and pixels in the 2D imaging plane.
The incidence field is a pixel-wise parametrization of the intrinsic invariant
to image cropping and resizing. With the estimated incidence field, a robust
RANSAC algorithm recovers intrinsic. We demonstrate the effectiveness of our
method by showing superior performance on synthetic and zero-shot testing
datasets. Beyond calibration, we demonstrate downstream applications in image
manipulation detection & restoration, uncalibrated two-view pose estimation,
and 3D sensing. Codes, models, and data will be held in
https://github.com/ShngJZ/WildCamera.
