We address the task of estimating camera parameters from a set of images depicting a
scene. Popular feature-based structure-from-motion (SfM) tools solve this task by …

In this paper, we present an efficient visual SLAM system designed to tackle both short-term
and long-term illumination challenges. Our system adopts a hybrid approach that combines …

In this work, we propose the use of Neural Radiance Fields (NeRF) as a scene
representation for visual localization. Recently, NeRF has been employed to enhance pose …

We propose FocusTune, a focus-guided sampling technique to improve the performance of
visual localization algorithms. FocusTune directs a scene coordinate regression model …

Scene coordinate regression (SCR) methods are a family of visual localization methods that
directly regress 2D-3D matches for camera pose estimation. They are effective in small …

Camera relocalization relies on 3D models of the scene with large memory footprint that is
incompatible with the memory budget of several applications. One solution to reduce the …

Pose regression networks predict the camera pose of a query image relative to a known
environment. Within this family of methods absolute pose regression (APR) has recently …

Camera localization methods based on retrieval, local feature matching, and 3D structure-
based pose estimation are accurate but require high storage, are slow, and are not privacy …

Scene coordinate regression (SCR) methods have emerged as a promising area of
research due to their potential for accurate visual localization. However, many existing SCR …

This study addresses the challenge of performing visual localization in demanding
conditions such as night-time scenarios adverse weather and seasonal changes. While …