@inproceedings{6a96c50bbae649158e1fa6245cbf56b5,
title = "Geometric Estimation via Robust Subspace Recovery",
abstract = "Geometric estimation from image point correspondences is the core procedure of many 3D vision problems, which is prevalently accomplished by random sampling techniques. In this paper, we consider the problem from an optimization perspective, to exploit the intrinsic linear structure of point correspondences to assist estimation. We generalize the conventional method to a robust one and extend the previous analysis for linear structure to develop several new algorithms. The proposed solutions essentially address the estimation problem by solving a subspace recovery problem to identify the inliers. Experiments on real-world image datasets for both fundamental matrix and homography estimation demonstrate the superiority of our method over the state-of-the-art in terms of both robustness and accuracy.",
keywords = "3D vision, Geometric estimation, Robust model fitting, Robust subspace recovery",
author = "Aoxiang Fan and Xingyu Jiang and Yang Wang and Junjun Jiang and Jiayi Ma",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 16th European Conference on Computer Vision, ECCV 2020 ; Conference date: 23-08-2020 Through 28-08-2020",
year = "2020",
doi = "10.1007/978-3-030-58542-6\_28",
language = "英语",
isbn = "9783030585419",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "462--478",
editor = "Andrea Vedaldi and Horst Bischof and Thomas Brox and Jan-Michael Frahm",
booktitle = "Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings",
address = "德国",
}