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 …

Object pose estimation is a core perception task that enables, for example, object
manipulation and scene understanding. The widely available, inexpensive, and high …

Object rearrangement is pivotal in robotic-environment interactions, representing a
significant capability in embodied AI. In this paper, we present SG-Bot, a novel …

We present the HANDAL dataset for category-level object pose estimation and affordance
prediction. Unlike previous datasets, ours is focused on robotics-ready manipulable objects …

Recent advances in neural radiance fields enable novel view synthesis of photo-realistic
images in dynamic settings, which can be applied to scenarios with human animation …

Category-level object pose estimation aiming to predict the 6D pose and 3D size of objects
from known categories typically struggles with large intra-class shape variation. Existing …

Objective: Deep learning-based object recognition, 6D pose estimation, and semantic scene
understanding require a large amount of training data to achieve generalization. Time …

We introduce HOT3D, a publicly available dataset for egocentric hand and object tracking in
3D. The dataset offers over 833 minutes (more than 3.7 M images) of multi-view …

Six-degree-of-freedom (6DoF) object pose estimation is a crucial task for virtual reality and
accurate robotic manipulation. Category-level 6DoF pose estimation has recently become …

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 …