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 …

We present a near real-time (10Hz) method for 6-DoF tracking of an unknown object from a
monocular RGBD video sequence, while simultaneously performing neural 3D …

This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM)
designed for embodied interaction, exploring a universal 3D object understanding with 3D …

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 …

We present HOI4D, a large-scale 4D egocentric dataset with rich annotations, to catalyze the
research of category-level human-object interaction. HOI4D consists of 2.4 M RGB-D …

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 …

Articulated object manipulation is a fundamental yet challenging task in robotics. Due to
significant geometric and semantic variations across object categories, previous …

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) …

Category-level 6D pose estimation aims to predict the poses and sizes of unseen objects
from a specific category. Thanks to prior deformation, which explicitly adapts a category …