Autonomous robots are increasingly served in real-world unstructured human environments
with complex long-horizon tasks, such as restaurant serving and office delivery. Task and …

Object pose detection and tracking has recently attracted increasing attention due to its wide
applications in many areas, such as autonomous driving, robotics, and augmented reality …

Invariance and equivariance to the rotation group have been widely discussed in the 3D
deep learning community for pointclouds. Yet most proposed methods either use complex …

Establishing correspondences from image to 3D has been a key task of 6DoF object pose
estimation for a long time. To predict pose more accurately, deeply learned dense maps …

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 …

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

Humans intuitively understand that inanimate objects do not move by themselves, but that
state changes are typically caused by human manipulation (eg, the opening of a book). This …

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 …

Category-level 6D object pose estimation aims to predict the position and orientation for
unseen objects, which plays a pillar role in many scenarios such as robotics and augmented …