In recent years a vast amount of visual content has been generated and shared from many
fields, such as social media platforms, medical imaging, and robotics. This abundance of …

We introduce LightGlue, a deep neural network that learns to match local features across
images. We revisit multiple design decisions of SuperGlue, the state of the art in sparse …

Neural fields have achieved impressive advancements in view synthesis and scene
reconstruction. However, editing these neural fields remains challenging due to the implicit …

While deep learning has recently achieved great success on multi-view stereo (MVS),
limited training data makes the trained model hard to be generalized to unseen scenarios …

Establishing correspondences between two images requires both local and global spatial
context. Given putative correspondences of feature points in two views, in this paper, we …

This work presents Kornia--an open source computer vision library which consists of a set of
differentiable routines and modules to solve generic computer vision problems. At its core …

This work focuses on mitigating two limitations in the joint learning of local feature detectors
and descriptors. First, the ability to estimate the local shape (scale, orientation, etc.) of …

Matching local features across images is a fundamental problem in computer vision.
Targeting towards high accuracy and efficiency, we propose Seeded Graph Matching …

Most existing studies on learning local features focus on the patch-based descriptions of
individual keypoints, whereas neglecting the spatial relations established from their keypoint …

Efficient and accurate identification of visual overlapping image pairs is an ongoing
challenge for large-scale Structure from Motion (SfM). Recently, CNN-based methods have …