We propose TRAM, a two-stage method to reconstruct a human's global trajectory and
motion from in-the-wild videos. TRAM robustifies SLAM to recover the camera motion in the …

We present SCube, a novel method for reconstructing large-scale 3D scenes (geometry,
appearance, and semantics) from a sparse set of posed images. Our method encodes …

Conventional frame camera is the mainstream sensor of the autonomous driving scene
perception, while it is limited in adverse conditions, such as low light. Event camera with …

In the field of monocular depth estimation (MDE), many models with excellent zero-shot
performance in general scenes emerge recently. However, these methods often fail in …

Humans effortlessly integrate common-sense knowledge with sensory input from vision and
touch to understand their surroundings. Emulating this capability, we introduce …

Environment sensing is a fundamental task in mobile augmented reality (AR). However, on-
device sensing and computing resources often limit mobile AR sensing capability, making …

We propose a framework for active next best view and touch selection for robotic
manipulators using 3D Gaussian Splatting (3DGS). 3DGS is emerging as a useful explicit …

Reactive collision avoidance is essential for agile robots navigating complex and dynamic
environments, enabling real-time obstacle response. However, this task is inherently …

The recent development of foundation models for monocular depth estimation such as
Depth Anything paved the way to zero-shot monocular depth estimation. Since it returns an …

Map-free relocalization technology is crucial for applications in autonomous navigation and
augmented reality, but relying on pre-built maps is often impractical. It faces significant …