# Geometric Image Correspondence Verification by Dense Pixel Matching

## TL;DR

This paper introduces a dense pixel matching approach for verifying geometric correspondence in image retrieval, improving re-ranking accuracy by combining local and global similarity metrics with geometric consistency.

## Contribution

It proposes a novel dense pixel matching and verification method that enhances image re-ranking and long-term visual localization, with architectural improvements to DGC-Net.

## Key findings

- Outperforms state-of-the-art image retrieval methods
- Demonstrates strong generalization across datasets
- Improves long-term visual localization accuracy

## Abstract

This paper addresses the problem of determining dense pixel correspondences between two images and its application to geometric correspondence verification in image retrieval. The main contribution is a geometric correspondence verification approach for re-ranking a shortlist of retrieved database images based on their dense pair-wise matching with the query image at a pixel level. We determine a set of cyclically consistent dense pixel matches between the pair of images and evaluate local similarity of matched pixels using neural network based image descriptors. Final re-ranking is based on a novel similarity function, which fuses the local similarity metric with a global similarity metric and a geometric consistency measure computed for the matched pixels. For dense matching our approach utilizes a modified version of a recently proposed dense geometric correspondence network (DGC-Net), which we also improve by optimizing the architecture. The proposed model and similarity metric compare favourably to the state-of-the-art image retrieval methods. In addition, we apply our method to the problem of long-term visual localization demonstrating promising results and generalization across datasets.