Building home assistant robots has long been a goal for vision and robotics researchers. To
achieve this task, a simulated environment with physically realistic simulation, sufficient …

We present PartNet: a consistent, large-scale dataset of 3D objects annotated with fine-
grained, instance-level, and hierarchical 3D part information. Our dataset consists of …

One of the fundamental goals of visual perception is to allow agents to meaningfully interact
with their environment. In this paper, we take a step towards that long-term goal--we extract …

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 explore a novel method to perceive and manipulate 3D articulated objects that
generalizes to enable a robot to articulate unseen classes of objects. We propose a vision …

Physical products are often complex assemblies combining a multitude of 3D parts modeled
in computer-aided design (CAD) software. CAD designers build up these assemblies by …

We address the task of simultaneous part-level reconstruction and motion parameter
estimation for articulated objects. Given two sets of multi-view images of an object in two …

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 …

This report surveys advances in deep learning‐based modelling techniques that address
four different 3D indoor scene analysis tasks, as well as synthesis of 3D indoor scenes. We …

Volumetric representation has been widely used for 3D deep learning in shape analysis due
to its generalization ability and regular data format. However, for fine-grained tasks like part …