Central to the application of many multi-view geometry algorithms is the extraction of matching points between multiple viewpoints, enabling classical tasks such as camera pose estimation and 3D reconstruction. Many approaches that characterize these points have been proposed based on hand-tuned appearance models or data-driven learning methods. We propose Soft Expectation and Deep Maximization (SEDM), an iterative unsupervised learning process that directly optimizes the repeatability of the features by posing the problem in a similar way to expectation maximization (EM). We found convergence to be reliable and the new model to be more lighting invariant and better at localize the underlying 3D points in a scene, improving SfM quality when compared to other state of the art deep learning detectors.
@article{arxiv.2104.10291,
title = {Soft Expectation and Deep Maximization for Image Feature Detection},
author = {Alexander Mai and Allen Yang and Dominique E. Meyer},
journal= {arXiv preprint arXiv:2104.10291},
year = {2021}
}