98 citations · 622 across the 36 of their papers we have counts for
5 papers · 1 filter
Surface Networks via General Covers
Niv Haim, Nimrod Segol, Heli Ben-Hamu +2
Developing deep learning techniques for geometric data is an active and fruitful research area. This paper tackles the problem of sphere-type surface learning by developing a novel…
Invariant and Equivariant Graph Networks
Haggai Maron, Heli Ben-Hamu, Nadav Shamir +1
Invariant and equivariant networks have been successfully used for learning images, sets, point clouds, and graphs. A basic challenge in developing such networks is finding the max…
(Probably) Concave Graph Matching
Haggai Maron, Yaron Lipman
In this paper we address the graph matching problem. Following the recent works of \cite{zaslavskiy2009path,Vestner2017} we analyze and generalize the idea of concave relaxations.…
Multi-chart Generative Surface Modeling
Heli Ben-Hamu, Haggai Maron, Itay Kezurer +2
This paper introduces a 3D shape generative model based on deep neural networks. A new image-like (i.e., tensor) data representation for genus-zero 3D shapes is devised. It is base…
Point Convolutional Neural Networks by Extension Operators
Matan Atzmon, Haggai Maron, Yaron Lipman
This paper presents Point Convolutional Neural Networks (PCNN): a novel framework for applying convolutional neural networks to point clouds. The framework consists of two operator…