14 citations · 16 across the 2 of their papers we have counts for
7 papers
Rethinking Sampling in 3D Point Cloud Generative Adversarial Networks
He Wang, Zetian Jiang, Li Yi +3
In this paper, we examine the long-neglected yet important effects of point sampling patterns in point cloud GANs. Through extensive experiments, we show that sampling-insensitive…
Learning 3D Part Assembly from a Single Image
Yichen Li, Kaichun Mo, Lin Shao +2
Autonomous assembly is a crucial capability for robots in many applications. For this task, several problems such as obstacle avoidance, motion planning, and actuator control have…
SAPIEN: A SimulAted Part-based Interactive ENvironment
Fanbo Xiang, Yuzhe Qin, Kaichun Mo +11
Building home assistant robots has long been a pursuit for vision and robotics researchers. To achieve this task, a simulated environment with physically realistic simulation, suff…
PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions
Kaichun Mo, He Wang, Xinchen Yan +1
3D generative shape modeling is a fundamental research area in computer vision and interactive computer graphics, with many real-world applications. This paper investigates the nov…
StructEdit: Learning Structural Shape Variations
Kaichun Mo, Paul Guerrero, Li Yi +4
Learning to encode differences in the geometry and (topological) structure of the shapes of ordinary objects is key to generating semantically plausible variations of a given shape…
StructureNet: Hierarchical Graph Networks for 3D Shape Generation
Kaichun Mo, Paul Guerrero, Li Yi +4
The ability to generate novel, diverse, and realistic 3D shapes along with associated part semantics and structure is central to many applications requiring high-quality 3D assets…