OperatorNet: Recovering 3D Shapes From Difference Operators

24 April 2019

Papers citing "OperatorNet: Recovering 3D Shapes From Difference Operators"

5 / 5 papers shown

Title
A Simple Strategy to Provable Invariance via Orbit Mapping Kanchana Vaishnavi Gandikota Jonas Geiping Zorah Lähner Adam Czapliñski Michael Moeller AAML 3DPC 18 3 0 24 Sep 2022
Flow-based Generative Models for Learning Manifold to Manifold Mappings Xingjian Zhen Rudrasis Chakraborty Liu Yang Vikas Singh DRL MedIm 31 9 0 18 Dec 2020
Instant recovery of shape from spectrum via latent space connections R. Marin Arianna Rampini U. Castellani Emanuele Rodolà M. Ovsjanikov Simone Melzi 35 20 0 14 Mar 2020
3D-CODED : 3D Correspondences by Deep Deformation Thibault Groueix Matthew Fisher Vladimir G. Kim Bryan C. Russell Mathieu Aubry 3DPC 3DV 132 325 0 13 Jun 2018
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 261 3,240 0 24 Nov 2016