  This work presents Kornia -- an open source computer vision library which
consists of a set of differentiable routines and modules to solve generic
computer vision problems. The package uses PyTorch as its main backend both for
efficiency and to take advantage of the reverse-mode auto-differentiation to
define and compute the gradient of complex functions. Inspired by OpenCV,
Kornia is composed of a set of modules containing operators that can be
inserted inside neural networks to train models to perform image
transformations, camera calibration, epipolar geometry, and low level image
processing techniques, such as filtering and edge detection that operate
directly on high dimensional tensor representations. Examples of classical
vision problems implemented using our framework are provided including a
benchmark comparing to existing vision libraries.
