Publication Type

Journal Article

Version

publishedVersion

Publication Date

12-2019

Abstract

Feature matching aims at generating correspondences across images, which is widely used in many computer vision tasks. Although considerable progress has been made on feature descriptors and fast matching for initial correspondence hypotheses, selecting good ones from them is still challenging and critical to the overall performance. More importantly, existing methods often take a long computational time, limiting their use in real-time applications. This paper attempts to separate true correspondences from false ones at high speed. We term the proposed method (GMS) grid-based motion Statistics, which incorporates the smoothness constraint into a statistic framework for separation and uses a grid-based implementation for fast calculation. GMS is robust to various challenging image changes, involving in viewpoint, scale, and rotation. It is also fast, e.g., take only 1 or 2 ms in a single CPU thread, even when 50K correspondences are processed. This has important implications for real-time applications. What’s more, we show that incorporating GMS into the classic feature matching and epipolar geometry estimation pipeline can significantly boost the overall performance. Finally, we integrate GMS into the well-known ORB-SLAM system for monocular initialization, resulting in a significant improvement.

Keywords

Feature matching, Epipolar geometry, Visual SLAM, Structure-from-motion, GMS

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

International Journal of Computer Vision

Volume

128

Issue

6

First Page

1580

Last Page

1593

ISSN

0920-5691

Publisher

Springer Verlag (Germany)

Embargo Period

3-28-2021

Additional URL

https://doi.org/10.1007/s11263-019-01280-3

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