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 …

While 6D object pose estimation has recently made a huge leap forward, most methods can
still only handle a single or a handful of different objects, which limits their applications. To …

For years, researchers have been devoted to generalizable object perception and
manipulation, where cross-category generalizability is highly desired yet underexplored. In …

Category-level 6D object pose estimation aims to predict the position and orientation for
unseen objects, which plays a pillar role in many scenarios such as robotics and augmented …

One critical challenge in 6D object pose estimation from a single RGBD image is efficient
integration of two different modalities, ie, color and depth. In this work, we tackle this problem …

Recently, 6DoF object pose estimation has become increasingly important for a broad range
of applications in the fields of virtual reality, augmented reality, autonomous driving, and …

We propose a new method for object pose estimation without CAD models. The previous
feature-matching-based method OnePose has shown promising results under a one-shot …

Trajectory prediction has been a crucial task in building a reliable autonomous driving
system by anticipating possible dangers. One key issue is to generate consistent trajectory …

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) …

It is difficult to precisely annotate object instances and their semantics in 3D space, and as
such, synthetic data are extensively used for these tasks, eg, category-level 6D object pose …