Positioning has recently received considerable attention as a key enabler in emerging
applications such as extended reality, unmanned aerial vehicles, and smart environments …

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 …

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 …

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 …

We introduce a novel framework for multiway point cloud mosaicking (named Wednesday)
designed to co-align sets of partially overlap** point clouds--typically obtained from 3D …

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 …

Local feature matching enjoys wide-ranging applications in the realm of computer vision,
encompassing domains such as image retrieval, 3D reconstruction, and object recognition …

In this paper we propose an efficient data-driven solution to self-localization within a
floorplan. Floorplan data is readily available long-term persistent and inherently robust to …