Generalizable 3D part segmentation is important but challenging in vision and robotics.
Training deep models via conventional supervised methods requires large-scale 3D …

Existing 3D scene understanding methods are heavily focused on 3D semantic and instance
segmentation. However identifying objects and their parts only constitutes an intermediate …

We present CARTO, a novel approach for reconstructing multiple articulated objects from a
single stereo RGB observation. We use implicit object-centric representations and learn a …

Abstract We propose Neural 3D Articulated object Prior (NAP), the first 3D deep generative
model to synthesize 3D articulated object models. Despite the extensive research on …

Given a 3D object, kinematic motion prediction aims to identify the mobile parts as well as
the corresponding motion parameters. Due to the large variations in both topological …

We present Cart, a new approach towards articulated-object manipulations by human
commands. Beyond the existing work that focuses on inferring articulation structures, we …

Despite much progress in large 3D datasets there are currently few interactive 3D object
datasets, and their scale is limited due to the manual effort required in their construction. We …

Reconstructing transparent objects using affordable RGB-D cameras is a persistent
challenge in robotic perception due to inconsistent appearances across views in the RGB …

Open-world 3D part segmentation is pivotal in diverse applications such as robotics and
AR/VR. Traditional supervised methods often grapple with limited 3D data availability and …

We present a neural network approach to transfer the motion from a single image of an
articulated object to a rest-state (ie, unarticulated) 3D model. Our network learns to predict …