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 …

Dense simultaneous localization and map** (SLAM) is crucial for robotics and augmented
reality applications. However current methods are often hampered by the non-volumetric or …

Recovering 3D structure and camera motion from images has been a long-standing focus of
computer vision research and is known as Structure-from-Motion (SfM). Solutions to this …

What will the future be? We wonder! In this survey, we explore the gap between current
research in egocentric vision and the ever-anticipated future, where wearable computing …

3D reconstruction aims to recover the dense 3D structure of a scene. It plays an essential
role in various applications such as Augmented/Virtual Reality (AR/VR), autonomous driving …

Automatic perception of human behaviors during social interactions is crucial for AR/VR
applications, and an essential component is estimation of plausible 3D human pose and …

Building 3D geometric maps of man-made spaces is a well-established and active field that
is fundamental to numerous computer vision and robotics applications. However …

From a single image, visual cues can help deduce intrinsic and extrinsic camera parameters
like the focal length and the gravity direction. This single-image calibration can benefit …

Despite the remarkable advances in image matching and pose estimation, image-based
localization of a camera in a temporally-varying outdoor environment is still a challenging …

Semantic 2D maps are commonly used by humans and machines for navigation purposes,
whether it's walking or driving. However, these maps have limitations: they lack detail, often …