FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis

16 June 2017

Papers citing "FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis"

7 / 7 papers shown

Title
Learning Feature Aggregation for Deep 3D Morphable Models Zhixiang Chen Tae-Kyun Kim 3DPC 3DH 45 24 0 05 May 2021
Learning Diverse Fashion Collocation by Neural Graph Filtering Xin Liu Yongbin Sun Ziwei Liu Dahua Lin 29 26 0 11 Mar 2020
Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling Wenkai Han Chenglu Wen Cheng-Yu Wang Xin Li Qing Li 3DPC 16 93 0 23 Dec 2019
PAN: Path Integral Based Convolution for Deep Graph Neural Networks Zheng Ma Ming Li Yuguang Wang GNN 22 24 0 24 Apr 2019
DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model Bo Wu Yang Liu B. Lang Lei Huang 27 68 0 10 Dec 2017
Deformable Shape Completion with Graph Convolutional Autoencoders Or Litany A. Bronstein M. Bronstein A. Makadia 25 222 0 01 Dec 2017
Geometric deep learning on graphs and manifolds using mixture model CNNs Federico Monti Davide Boscaini Jonathan Masci Emanuele Rodolà Jan Svoboda M. Bronstein GNN 260 1,811 0 25 Nov 2016