MODS: Fast and Robust Method for Two-View Matching

TL;DR

MODS is a fast, robust algorithm for wide-baseline image matching that adapts its computational effort through view synthesis and improved correspondence selection, outperforming existing methods in challenging scenarios.

Contribution

The paper introduces MODS, a novel wide-baseline matching algorithm combining view synthesis and an improved correspondence method, achieving superior robustness and speed.

Findings

MODS outperforms state-of-the-art methods in robustness and speed.

The modified correspondence rule increases correct matches by 5-20%.

MODS handles diverse challenging two-view problems effectively.

Abstract

A novel algorithm for wide-baseline matching called MODS - Matching On Demand with view Synthesis - is presented. The MODS algorithm is experimentally shown to solve a broader range of wide-baseline problems than the state of the art while being nearly as fast as standard matchers on simple problems. The apparent robustness vs. speed trade-off is finessed by the use of progressively more time-consuming feature detectors and by on-demand generation of synthesized images that is performed until a reliable estimate of geometry is obtained. We introduce an improved method for tentative correspondence selection, applicable both with and without view synthesis. A modification of the standard first to second nearest distance rule increases the number of correct matches by 5-20% at no additional computational cost. Performance of the MODS algorithm is evaluated on several standard publicly available datasets, and on a new set of geometrically challenging wide baseline problems that is made public together with the ground truth. Experiments show that the MODS outperforms the state-of-the-art in robustness and speed. Moreover, MODS performs well on other classes of difficult two-view problems like matching of images from different modalities, with wide temporal baseline or with significant lighting changes.