Object pose detection and tracking has recently attracted increasing attention due to its wide
applications in many areas, such as autonomous driving, robotics, and augmented reality …

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 …

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 …

Abstract 6D object pose estimation networks are limited in their capability to scale to large
numbers of object instances due to the close-set assumption and their reliance on high …

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 …

Rotation estimation of high precision from an RGB-D object observation is a huge challenge
in 6D object pose estimation, due to the difficulty of learning in the non-linear space of SO …

Category-level object pose estimation aims to predict the 6D pose as well as the 3D metric
size of arbitrary objects from a known set of categories. Recent methods harness shape prior …

Image retrieval-based cross-view localization methods often lead to very coarse camera
pose estimation, due to the limited sampling density of the database satellite images. In this …