MatchAnything: Universal Cross-Modality Image Matching with Large-Scale
  Pre-Training
Image matching, which aims to identify corresponding pixel locations between
images, is crucial in a wide range of scientific disciplines, aiding in image
registration, fusion, and analysis. In recent years, deep learning-based image
matching algorithms have dramatically outperformed humans in rapidly and
accurately finding large amounts of correspondences. However, when dealing with
images captured under different imaging modalities that result in significant
appearance changes, the performance of these algorithms often deteriorates due
to the scarcity of annotated cross-modal training data. This limitation hinders
applications in various fields that rely on multiple image modalities to obtain
complementary information. To address this challenge, we propose a large-scale
pre-training framework that utilizes synthetic cross-modal training signals,
incorporating diverse data from various sources, to train models to recognize
and match fundamental structures across images. This capability is transferable
to real-world, unseen cross-modality image matching tasks. Our key finding is
that the matching model trained with our framework achieves remarkable
generalizability across more than eight unseen cross-modality registration
tasks using the same network weight, substantially outperforming existing
methods, whether designed for generalization or tailored for specific tasks.
This advancement significantly enhances the applicability of image matching
technologies across various scientific disciplines and paves the way for new
applications in multi-modality human and artificial intelligence analysis and
beyond.
67880298f2b3aef29aea2777
Xingyi He
67880298f2b3aef29aea2778
Hao Yu
67880298f2b3aef29aea2779
Sida Peng
67880298f2b3aef29aea277a
Dongli Tan
67880298f2b3aef29aea277b
Zehong Shen
67880298f2b3aef29aea277c
Hujun Bao
67880298f2b3aef29aea277d
Xiaowei Zhou
MatchAnything: Universal Cross-Modality Image Matching with Large-Scale
  Pre-Training
Image matching, which aims to identify corresponding pixel locations between
images, is crucial in a wide range of scientific disciplines, aiding in image
registration, fusion, and analysis. In recent years, deep learning-based image
matching algorithms have dramatically outperformed humans in rapidly and
accurately finding large amounts of correspondences. However, when dealing with
images captured under different imaging modalities that result in significant
appearance changes, the performance of these algorithms often deteriorates due
to the scarcity of annotated cross-modal training data. This limitation hinders
applications in various fields that rely on multiple image modalities to obtain
complementary information. To address this challenge, we propose a large-scale
pre-training framework that utilizes synthetic cross-modal training signals,
incorporating diverse data from various sources, to train models to recognize
and match fundamental structures across images. This capability is transferable
to real-world, unseen cross-modality image matching tasks. Our key finding is
that the matching model trained with our framework achieves remarkable
generalizability across more than eight unseen cross-modality registration
tasks using the same network weight, substantially outperforming existing
methods, whether designed for generalization or tailored for specific tasks.
This advancement significantly enhances the applicability of image matching
technologies across various scientific disciplines and paves the way for new
applications in multi-modality human and artificial intelligence analysis and
beyond.
A pre-training framework using synthetic cross-modal signals enhances the generalizability of deep learning image matching across different imaging modalities.
image matching deep learning cross-modal training pre-training synthetic cross-modal training signals fundamental structures generalizability cross-modality registration tasks