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 …

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 …

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 …

Zero-shot 6D object pose estimation involves the detection of novel objects with their 6D
poses in cluttered scenes presenting significant challenges for model generalizability …

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) …

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 …

We present GigaPose a fast robust and accurate method for CAD-based novel object pose
estimation in RGB images. GigaPose first leverages discriminative" templates" rendered …

Current 6D pose estimation methods focus on handling objects that are previously trained,
which limits their applications in real dynamic world. To this end, we propose a geometry …

In this work we present a novel dense-correspondence method for 6DoF object pose
estimation from a single RGB-D image. While many existing data-driven methods achieve …

Recent learning methods for object pose estimation require resource-intensive training for
each individual object instance or category hampering their scalability in real applications …