2.9k citations · 3.1k across the 32 of their papers we have counts for
8 papers · 1 filter
Compositionally Generalizable 3D Structure Prediction
Songfang Han, Jiayuan Gu, Kaichun Mo +4
Single-image 3D shape reconstruction is an important and long-standing problem in computer vision. A plethora of existing works is constantly pushing the state-of-the-art performan…
DSG-Net: Learning Disentangled Structure and Geometry for 3D Shape Generation
Jie Yang, Kaichun Mo, Yu-Kun Lai +2
D shape generation is a fundamental operation in computer graphics. While significant progress has been made, especially with recent deep generative models, it remains a challenge…
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…
Generative 3D Part Assembly via Dynamic Graph Learning
Jialei Huang, Guanqi Zhan, Qingnan Fan +5
Autonomous part assembly is a challenging yet crucial task in 3D computer vision and robotics. Analogous to buying an IKEA furniture, given a set of 3D parts that can assemble a si…
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…
Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories
Tiange Luo, Kaichun Mo, Zhiao Huang +4
We address the problem of discovering 3D parts for objects in unseen categories. Being able to learn the geometry prior of parts and transfer this prior to unseen categories pose f…