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

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 …

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 …

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 …

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

LiDAR mapping is important yet challenging in self-driving and mobile robotics. To tackle
such a global point cloud registration problem, DeepMapping converts the complex map …

Understanding the world in first-person view is fundamental in Augmented Reality (AR). This
immersive perspective brings dramatic visual changes and unique challenges compared to …

We tackle the problem of estimating a Manhattan frame, ie three orthogonal vanishing
points, and the unknown focal length of the camera, leveraging a prior vertical direction. The …