62 citations · 131 across the 7 of their papers we have counts for
9 papers
Frame Averaging for Equivariant Shape Space Learning
Matan Atzmon, Koki Nagano, Sanja Fidler +2
The task of shape space learning involves mapping a train set of shapes to and from a latent representation space with good generalization properties. Often, real-world collections…
Frame Averaging for Invariant and Equivariant Network Design
Omri Puny, Matan Atzmon, Heli Ben-Hamu +4
Many machine learning tasks involve learning functions that are known to be invariant or equivariant to certain symmetries of the input data. However, it is often challenging to de…
Augmenting Implicit Neural Shape Representations with Explicit Deformation Fields
Matan Atzmon, David Novotny, Andrea Vedaldi +1
Implicit neural representation is a recent approach to learn shape collections as zero level-sets of neural networks, where each shape is represented by a latent code. So far, the…
Isometric Autoencoders
Amos Gropp, Matan Atzmon, Yaron Lipman
High dimensional data is often assumed to be concentrated on or near a low-dimensional manifold. Autoencoders (AE) is a popular technique to learn representations of such data by p…
SALD: Sign Agnostic Learning with Derivatives
Matan Atzmon, Yaron Lipman
Learning 3D geometry directly from raw data, such as point clouds, triangle soups, or unoriented meshes is still a challenging task that feeds many downstream computer vision and g…
Implicit Geometric Regularization for Learning Shapes
Amos Gropp, Lior Yariv, Niv Haim +2
Representing shapes as level sets of neural networks has been recently proved to be useful for different shape analysis and reconstruction tasks. So far, such representations were…
SAL: Sign Agnostic Learning of Shapes from Raw Data
Matan Atzmon, Yaron Lipman
Recently, neural networks have been used as implicit representations for surface reconstruction, modelling, learning, and generation. So far, training neural networks to be implici…
Controlling Neural Level Sets
Matan Atzmon, Niv Haim, Lior Yariv +3
The level sets of neural networks represent fundamental properties such as decision boundaries of classifiers and are used to model non-linear manifold data such as curves and surf…
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…