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.
