Image Matching Benchmark

Image matching has been one of the most fundamental and active research areas in computer vision community. In this field, existing evaluation protocols overemphasize feature design and ignore the performance of image level matching. However, the latter is critical for many high-level vision and robotics tasks. To address this, we present the first application-oriented image matching benchmark to facilitate the analysis of matching algorithms in application level. In addition, we construct a large-scale dataset of real-world images that covers a wide range of scene types, on which stateof-the-art matchers (local features with correspondence selection methods) are exhaustively evaluated and analyzed. Moreover, we demonstrate the effectiveness of a simple technique which is readily pluggable into any matching system to improve performance. The results and analysis will provide researchers with a practical guide to utilizing and improving image matching. Finally, we will release our evaluation code on the GitHub.