An Evaluation of Feature Matchers for Fundamental Matrix Estimation

JiaWang Bian (The University of Adelaide), Yu-Huan Wu (Nankai University), Ji Zhao (TuSimple), Yun Liu (Nankai University), Le Zhang (Institute for Infocomm Research´╝îAgency for Science, Technology and Research (ASTAR)), Ming-Ming Cheng (Nankai University), Ian Reid (University of Adelaide)

Matching two images while estimating their relative geometry is a key step in many computer vision applications. For decades, a well-established pipeline, consisting of SIFT, RANSAC, and 8-point algorithm, has been used for this task. Recently, many new approaches were proposed and shown to outperform previous alternatives on standard benchmarks, including the learned features, correspondence pruning algorithms, and robust estimators. However, whether it is beneficial to incorporate them into the classic pipeline is less-investigated. To this end, we are interested in i) evaluating the performance of these recent algorithms in the context of image matching and epipolar geometry estimation, and ii) leveraging them to design more practical registration systems. The experiments are conducted in four large-scale datasets using strictly defined evaluation metrics, and the promising results provide insight into which algorithms suit which scenarios. According to this, we propose three high-quality matching systems and a Coarse-to-Fine RANSAC estimator. They show remarkable performances and have potentials to a large part of computer vision tasks. To facilitate future research, the full evaluation pipeline and the proposed methods are made publicly available.


Paper (PDF)

title={An Evaluation of Feature Matchers for Fundamental Matrix Estimation},
author={JiaWang Bian and Yu-Huan Wu and Ji Zhao and Yun Liu and Le Zhang and Ming-Ming Cheng and Ian Reid},
booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
publisher={BMVA Press},
editor={Kirill Sidorov and Yulia Hicks},