Learning to Find Good Correspondences

Kwang Moo Yi, Eduard Trulls, Yuki Ono, Vincent Lepetit, Mathieu Salzmann, Pascal Fua

Research output: Contribution to journalArticle

Abstract

We develop a deep architecture to learn to find good correspondences for wide-baseline stereo. Given a set of putative sparse matches and the camera intrinsics, we train our network in an end-to-end fashion to label the correspondences as inliers or outliers, while simultaneously using them to recover the relative pose, as encoded by the essential matrix. Our architecture is based on a multi-layer perceptron operating on pixel coordinates rather than directly on the image, and is thus simple and small. We introduce a novel normalization technique, called Context Normalization, which allows us to process each data point separately while imbuing it with global information, and also makes the network invariant to the order of the correspondences. Our experiments on multiple challenging datasets demonstrate that our method is able to drastically improve the state of the art with little training data.
Original languageEnglish
Number of pages13
JournalarXiv.org e-Print archive
Publication statusPublished - 2018
EventIEEE Conference on Computer Vision and Pattern Recognition, 2016: 2018 IEEE Conference on Computer Vision and Pattern Recognition - Salt Lake City, United States
Duration: 18 Jun 201822 Jun 2018

Keywords

  • cs.CV

Cite this

Yi, K. M., Trulls, E., Ono, Y., Lepetit, V., Salzmann, M., & Fua, P. (2018). Learning to Find Good Correspondences. arXiv.org e-Print archive.