62 citations · 118 across the 15 of their papers we have counts for
7 papers · 1 filter
CAD: Photorealistic 3D Generation via Adversarial Distillation
Ziyu Wan, Despoina Paschalidou, Ian Huang +5
The increased demand for 3D data in AR/VR, robotics and gaming applications, gave rise to powerful generative pipelines capable of synthesizing high-quality 3D objects. Most of the…
SAGE: Bridging Semantic and Actionable Parts for GEneralizable Manipulation of Articulated Objects
Haoran Geng, Songlin Wei, Congyue Deng +3
To interact with daily-life articulated objects of diverse structures and functionalities, understanding the object parts plays a central role in both user instruction comprehensio…
Make a Donut: Hierarchical EMD-Space Planning for Zero-Shot Deformable Manipulation with Tools
Yang You, Bokui Shen, Congyue Deng +4
Deformable object manipulation stands as one of the most captivating yet formidable challenges in robotics. While previous techniques have predominantly relied on learning latent d…
PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking
Yang Zheng, Adam W. Harley, Bokui Shen +2
We introduce PointOdyssey, a large-scale synthetic dataset, and data generation framework, for the training and evaluation of long-term fine-grained tracking algorithms. Our goal i…
Banana: Banach Fixed-Point Network for Pointcloud Segmentation with Inter-Part Equivariance
Congyue Deng, Jiahui Lei, Bokui Shen +2
Equivariance has gained strong interest as a desirable network property that inherently ensures robust generalization. However, when dealing with complex systems such as articulate…
NAP: Neural 3D Articulation Prior
Jiahui Lei, Congyue Deng, Bokui Shen +2
We propose Neural 3D Articulation Prior (NAP), the first 3D deep generative model to synthesize 3D articulated object models. Despite the extensive research on generating 3D object…
GINA-3D: Learning to Generate Implicit Neural Assets in the Wild
Bokui Shen, Xinchen Yan, Charles R. Qi +5
Modeling the 3D world from sensor data for simulation is a scalable way of developing testing and validation environments for robotic learning problems such as autonomous driving.…