Multimodal image matching, which refers to identifying and then corresponding the same or
similar structure/content from two or more images that are of significant modalities or …

Finding correspondences between images is a fundamental problem in computer vision. In
this paper, we show that correspondence emerges in image diffusion models without any …

This paper introduces SuperGlue, a neural network that matches two sets of local features
by jointly finding correspondences and rejecting non-matchable points. Assignments are …

Abstract Neural Radiance Fields (NeRF) have recently gained a surge of interest within the
computer vision community for its power to synthesize photorealistic novel views of real …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

We introduce DROID-SLAM, a new deep learning based SLAM system. DROID-SLAM
consists of recurrent iterative updates of camera pose and pixelwise depth through a Dense …

Text-to-image diffusion models have made significant advances in generating and editing
high-quality images. As a result, numerous approaches have explored the ability of diffusion …

Interest point detection and local feature description are fundamental steps in many
computer vision applications. Classical approaches are based on a detect-then-describe …

Robust and accurate visual localization is a fundamental capability for numerous
applications, such as autonomous driving, mobile robotics, or augmented reality. It remains …

In this work we address the problem of finding reliable pixel-level correspondences under
difficult imaging conditions. We propose an approach where a single convolutional neural …