20 citations · 37 across the 3 of their papers we have counts for
6 papers
Predictive and Generative Neural Networks for Object Functionality
Ruizhen Hu, Zihao Yan, Jingwen Zhang +4
Humans can predict the functionality of an object even without any surroundings, since their knowledge and experience would allow them to "hallucinate" the interaction or usage sce…
AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss
Chenyang Zhu, Kai Xu, Siddhartha Chaudhuri +3
We introduce AdaCoSeg, a deep neural network architecture for adaptive co-segmentation of a set of 3D shapes represented as point clouds. Differently from the familiar single-insta…
Learning Implicit Fields for Generative Shape Modeling
Zhiqin Chen, Hao Zhang
We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the…
SCORES: Shape Composition with Recursive Substructure Priors
Chenyang Zhu, Kai Xu, Siddhartha Chaudhuri +2
We introduce SCORES, a recursive neural network for shape composition. Our network takes as input sets of parts from two or more source 3D shapes and a rough initial placement of t…
Evaluating the accuracy of the dynamic mode decomposition
Hao Zhang, Scott T. M. Dawson, Clarence W. Rowley +2
Dynamic mode decomposition (DMD) gives a practical means of extracting dynamic information from data, in the form of spatial modes and their associated frequencies and growth/decay…
GRASS: Generative Recursive Autoencoders for Shape Structures
Jun Li, Kai Xu, Siddhartha Chaudhuri +3
We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures. Our key insight is that 3D shapes are effectively character…