We present a near real-time (10Hz) method for 6-DoF tracking of an unknown object from a
monocular RGBD video sequence, while simultaneously performing neural 3D …

We propose a new method named OnePose for object pose estimation. Unlike existing
instance-level or category-level methods, OnePose does not rely on CAD models and can …

We present HOI4D, a large-scale 4D egocentric dataset with rich annotations, to catalyze the
research of category-level human-object interaction. HOI4D consists of 2.4 M RGB-D …

As the basis for prehensile manipulation, it is vital to enable robots to grasp as robustly as
humans. Our innate grasping system is prompt, accurate, flexible, and continuous across …

We present FoundationPose a unified foundation model for 6D object pose estimation and
tracking supporting both model-based and model-free setups. Our approach can be instantly …

In this paper, we present a generalizable model-free 6-DoF object pose estimator called
Gen6D. Existing generalizable pose estimators either need the high-quality object models or …

Capturing the interactions between humans and their environment in 3D is important for
many applications in robotics, graphics, and vision. Recent works to reconstruct the 3D …

Our method studies the complex task of object-centric 3D understanding from a single RGB-
D observation. As it is an ill-posed problem, existing methods suffer from low performance …

We present a new dataset for 6-DoF pose estimation of known objects, with a focus on
robotic manipulation research. We propose a set of toy grocery objects, whose physical …

In this paper, we focus on the problem of category-level object pose estimation, which is
challenging due to the large intra-category shape variation. 3D graph convolution (3D-GC) …