High-quality 3D reconstruction is an important topic in computer graphics and computer
vision with many applications, such as robotics and augmented reality. The advent 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 …

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 …

We present Theseus, an efficient application-agnostic open source library for differentiable
nonlinear least squares (DNLS) optimization built on PyTorch, providing a common …

Finding local features that are repeatable across multiple views is a cornerstone of sparse
3D reconstruction. The classical image matching paradigm detects keypoints per-image …

Multi-view stereo reconstruction (MVS) in the wild requires to first estimate the camera
intrinsic and extrinsic parameters. These are usually tedious and cumbersome to obtain yet …

Humans can orient themselves in their 3D environments using simple 2D maps. Differently,
algorithms for visual localization mostly rely on complex 3D point clouds that are expensive …

Virtual reality, augmented reality, robotics, and autonomous driving have attracted much
attention from the academic and industrial communities, in which visual SLAM (VSLAM) is a …

Abstract Neural Radiance Fields (NeRF) have recently demonstrated photorealistic results
for the task of novel view synthesis. In this paper, we propose to apply novel view synthesis …

This paper addresses the problem of vehicle-mounted camera localization by matching a
ground-level image with an overhead-view satellite map. Existing methods often treat this …