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 …

We introduce a scalable approach for object pose estimation trained on simulated RGB
views of multiple 3D models together. We learn an encoding of object views that does not …

We present a novel one-shot method for object detection and 6 DoF pose estimation, that
does not require training on target objects. At test time, it takes as input a target image and a …

The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances
in an RGB-D image. Contrary to" instance-level" 6D pose estimation tasks, our problem …

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 …

Category-level 6D object pose estimation aims to estimate the rotation translation and size
of unseen instances within specific categories. In this area dense correspondence-based …

Estimating the pose of an unseen object is the goal of the challenging one-shot pose
estimation task. Previous methods have heavily relied on feature matching with great …

As robotic systems increasingly encounter complex and unconstrained real-world scenarios,
there is a demand to recognize diverse objects. The state-of-the-art 6D object pose …

Most recent 6D pose estimation frameworks first rely on a deep network to establish
correspondences between 3D object keypoints and 2D image locations and then use a …

Abstract 6D object pose estimation is crucial in the field of computer vision. However, it
suffers from a significant lack of large-scale and diverse datasets, impeding comprehensive …