Improve Image Matching Pipeline Using Model-Based Approach

Abstract

Image matching is one of many fundamental tasks in computer vision with a wide range of applications, including image registration, camera pose estimation and 3D reconstruction. Traditional image matching techniques rely on local image features such as SIFT, ORB, KAZE, AKAZE, and BRISK. While these techniques are fast, efficient and generalizable they often struggle under challenging conditions, such as significant changes in viewpoint or varying illumination. To address these challenges, we introduce PatchMatch, an image matching pipeline designed to enhance robust ness and accuracy in difficult scenarios. PatchMatch leverages the strengths of tra ditional local image features for the initial feature detection step and incorporates a quantized lightweight CNN-based model to improve feature matching. This hy brid approach combines the speed, efficiency and generalizability of classical methods with the advanced matching capabilities of deep learning, resulting in a robust and efficient solution for image matching under challenging conditions. The PatchMatch pipeline demonstrates superior performance in terms of matching accuracy and ro bustness, especially in cases with large viewpoint changes and illumination varia tions. Through extensive experimentation and evaluation, we show that PatchMatch significantly outperforms traditional techniques, paving the way for more reliable im age matching in real-world applications.