  We present a deep convolutional neural network for estimating the relative
homography between a pair of images. Our feed-forward network has 10 layers,
takes two stacked grayscale images as input, and produces an 8 degree of
freedom homography which can be used to map the pixels from the first image to
the second. We present two convolutional neural network architectures for
HomographyNet: a regression network which directly estimates the real-valued
homography parameters, and a classification network which produces a
distribution over quantized homographies. We use a 4-point homography
parameterization which maps the four corners from one image into the second
image. Our networks are trained in an end-to-end fashion using warped MS-COCO
images. Our approach works without the need for separate local feature
detection and transformation estimation stages. Our deep models are compared to
a traditional homography estimator based on ORB features and we highlight the
scenarios where HomographyNet outperforms the traditional technique. We also
describe a variety of applications powered by deep homography estimation, thus
showcasing the flexibility of a deep learning approach.
