Feature matching is an important computer vision task that involves estimating
correspondences between two images of a 3D scene and dense methods estimate all such …

Identifying robust and accurate correspondences across images is a fundamental problem
in computer vision that enables various downstream tasks. Recent semi-dense matching …

Local feature matching enjoys wide-ranging applications in the realm of computer vision,
encompassing domains such as image retrieval, 3D reconstruction, and object recognition …

This study tackles image matching in difficult scenarios, such as scenes with significant
variations or limited texture, with a strong emphasis on computational efficiency. Previous …

Feature matching is a crucial task in the field of computer vision which involves finding
correspondences between images. Previous studies achieve remarkable performance using …

We propose a way to train deep learning based keypoint descriptors that makes them
approximately equivariant for locally affine transformations of the image plane. The main …

Homography estimation is the task of determining the transformation from an image pair. Our
approach focuses on employing detector-free feature matching methods to address this …

Recent advancements in learning-based Multi-View Stereo (MVS) methods have
prominently featured transformer-based models with attention mechanisms. However …

Transformer has been widely used in hyperspectral image (HSI) classification tasks because
of its ability to capture long-range dependencies. However, most Transformer-based …

We propose a cluster-based sparse graph network to improve the efficiency of image feature
matching. This architecture clusters keypoints with high correlations into the same …