  Geometric camera calibration is often required for applications that
understand the perspective of the image. We propose perspective fields as a
representation that models the local perspective properties of an image.
Perspective Fields contain per-pixel information about the camera view,
parameterized as an up vector and a latitude value. This representation has a
number of advantages as it makes minimal assumptions about the camera model and
is invariant or equivariant to common image editing operations like cropping,
warping, and rotation. It is also more interpretable and aligned with human
perception. We train a neural network to predict Perspective Fields and the
predicted Perspective Fields can be converted to calibration parameters easily.
We demonstrate the robustness of our approach under various scenarios compared
with camera calibration-based methods and show example applications in image
compositing.
