@inproceedings{0effc4c2bad341ae8c43e6cdfe7b087a,
title = "ARSAC: Robust model estimation via adaptively ranked sample consensus",
abstract = "RANSAC is a popular model estimation algorithm in various of computer vision applications. However, it easily gets slow as the inlier rate of the measurements declines. In this paper, a novel Adaptively Ranked Sample Consensus (ARSAC) algorithm is presented to boost the speed and robustness of RANSAC. Our algorithm adopts non-uniform sampling based on the ranked measurements. We propose an adaptive scheme which updates the ranking of the measurements on each trial, to incorporate high quality measurement into sample at high priority. We also design a geometric constraint during sampling process, which could alleviate degenerate cases caused by non-uniform sampling in epipolar geometry. Experiments on real-world data demonstrate the effectiveness and robustness of the proposed method compared to the state-of-the-art methods.",
keywords = "Adaptively ranked measurements, Efficiency, Geometric constraint, Model estimation, Non-uniform sampling",
author = "Rui Li and Jinqiu Sun and Yu Zhu and Haisen Li and Yanning Zhang",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2017.; 2nd Chinese Conference on Computer Vision, CCCV 2017 ; Conference date: 11-10-2017 Through 14-10-2017",
year = "2017",
doi = "10.1007/978-981-10-7302-1\_49",
language = "英语",
isbn = "9789811073014",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "591--602",
editor = "Liang Wang and Xiang Bai and Jinfeng Yang and Qingshan Liu and Deyu Meng and Qinghua Hu and Ming-Ming Cheng",
booktitle = "Computer Vision - 2nd CCF Chinese Conference, CCCV 2017, Proceedings",
}