10 citations · 22 across the 3 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
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…