Deep Image Homography Estimation
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.
ArXiv