Object pose estimation is a fundamental computer vision problem with broad applications in
augmented reality and robotics. Over the past decade, deep learning models, due to their …

Estimating 6D object poses is a major challenge in 3D computer vision. Building on
successful instance-level approaches research is shifting towards category-level pose …

Estimating the pose of objects through vision is essential to make robotic platforms interact
with the environment. Yet, it presents many challenges, often related to the lack of flexibility …

While RGBD-based methods for category-level object pose estimation hold promise, their
reliance on depth data limits their applicability in diverse scenarios. In response, recent …

In the realm of computer vision, the perception and reconstruction of the 3D world through
vision signals heavily rely on camera intrinsic parameters, which have long been a subject …

A key challenge in model-free category-level pose estimation is the extraction of contextual
object features that generalize across varying instances within a specific category. Recent …

Object pose estimation, crucial in computer vision and robotics applications, faces
challenges with the diversity of unseen categories. We propose a zero-shot method to …

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling
augmented reality and robotic manipulation. Category-level methods have received …

Category-level object pose estimation aims to recover the rotation, translation and size of
unseen instances within predefined categories. In this task, deep neural network-based …