Feature matching plays a crucial role in computer vision, with applications in visual
localization, simultaneous localization and map** (SLAM), image stitching, and more. It …

As with any data fusion task, the front-end of the pipeline for image fusion, aiming to collect
multitudinous physical properties from multimodal images taken by different types of …

Recovering camera pose from two-view images is a critical problem in photogrammetry and
computer vision. For complex scenarios, point correspondences that are constructed by off …

In two-view correspondence learning, prevalent multi-layer perceptron (MLP)-based
methods struggle with context capturing. To remedy this issue, recent advances innovatively …

Recent advances in two-view correspondence learning consider aggregating local
contextual information from k-nearest neighbors by exploring local geometric extractors …

The registration of multimodal satellite images is essential for a prerequisite for accruing
complementary observational data. Nevertheless, the differential imaging nuances amongst …

The coregistration of optical and synthetic aperture radar (SAR) imageries is the bottleneck
in exploring the complementary information from the two multimodal datasets. The difficulties …

Learning based feature matching methods have been commonly studied in recent years.
The core issue for learning feature matching is to how to learn (1) discriminative …

Verifying the neighborhood consensus to remove false correspondence is a popular idea in
feature matching. However, traditional neighborhood consensus only considers spatial …

Change detection (CD) methods work on the basis of co-registered multi-temporal images
with equivalent resolutions. Due to the limitation of sensor imaging conditions and revisit …