2 citations · 2 across the 1 of their papers we have counts for
3 papers
cs.GR2020★ 2 cited
FAME: 3D Shape Generation via Functionality-Aware Model Evolution
Yanran Guan, Han Liu, Kun Liu +8
We introduce a modeling tool which can evolve a set of 3D objects in a functionality-aware manner. Our goal is for the evolution to generate large and diverse sets of plausible 3D…
cs.CV2019
PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation
Fenggen Yu, Kun Liu, Yan Zhang +2
Deep learning approaches to 3D shape segmentation are typically formulated as a multi-class labeling problem. Existing models are trained for a fixed set of labels, which greatly l…
cs.CV2017
3D Shape Segmentation via Shape Fully Convolutional Networks
Pengyu Wang, Yuan Gan, Panpan Shui +4
We desgin a novel fully convolutional network architecture for shapes, denoted by Shape Fully Convolutional Networks (SFCN). 3D shapes are represented as graph structures in the SF…