D2D: Learning to find good correspondences for image matching and manipulation

07/16/2020 ∙ by Olivia Wiles, et al. ∙ 10

We propose a new approach to determining correspondences between image pairs under large changes in illumination, viewpoint, context, and material. While most approaches seek to extract a set of reliably detectable regions in each image which are then compared (sparse-to-sparse) using increasingly complicated or specialized pipelines, we propose a simple approach for matching all points between the images (dense-to-dense) and subsequently selecting the best matches. The two key parts of our approach are: (i) to condition the learned features on both images, and (ii) to learn a distinctiveness score which is used to choose the best matches at test time. We demonstrate that our model can be used to achieve state of the art or competitive results on a wide range of tasks: local matching, camera localization, 3D reconstruction, and image stylization.



There are no comments yet.


page 1

page 6

page 8

page 15

page 16

page 17

page 18

page 19

Code Repositories


Official repository of 'Co-Attention for Conditioned Image Matching'

view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.