Silver medal Solution for Image Matching Challenge 2024

TL;DR

This paper presents a comprehensive image matching pipeline for 3D mapping challenges, integrating multiple advanced techniques to improve robustness across diverse environmental conditions.

Contribution

It introduces a novel pipeline combining pre-trained networks, keypoint detection, and matching algorithms, achieving high performance on the Image Matching Challenge 2024.

Findings

Achieved a score of 0.167 on the private leaderboard.

Combining KeyNetAffNetHardNet and SuperPoint improves keypoint detection.

Method effectively handles challenging variations in surface texture and environment.

Abstract

Image Matching Challenge 2024 is a competition focused on building 3D maps from diverse image sets, requiring participants to solve fundamental computer vision challenges in image matching across varying angles, lighting, and seasonal changes. This project develops a Pipeline method that combines multiple advanced techniques: using pre-trained EfficientNet-B7 for initial feature extraction and cosine distance-based image pair filtering, employing both KeyNetAffNetHardNet and SuperPoint for keypoint feature extraction, utilizing AdaLAM and SuperGlue for keypoint matching, and finally applying Pycolmap for 3D spatial analysis. The methodology achieved an excellent score of 0.167 on the private leaderboard, with experimental results demonstrating that the combination of KeyNetAffNetHardNet and SuperPoint provides significant advantages in keypoint detection and matching, particularly when dealing with challenging variations in surface texture and environmental conditions that typically degrade traditional algorithm performance.