Search Results for author: Matan Atzmon

Found 12 papers, 6 papers with code

Approximately Piecewise E(3) Equivariant Point Networks

no code implementations13 Feb 2024 Matan Atzmon, Jiahui Huang, Francis Williams, Or Litany

Integrating a notion of symmetry into point cloud neural networks is a provably effective way to improve their generalization capability.

Uncertainty Quantification

Neural Kernel Surface Reconstruction

no code implementations CVPR 2023 Jiahui Huang, Zan Gojcic, Matan Atzmon, Or Litany, Sanja Fidler, Francis Williams

We present a novel method for reconstructing a 3D implicit surface from a large-scale, sparse, and noisy point cloud.

Surface Reconstruction

Frame Averaging for Equivariant Shape Space Learning

no code implementations CVPR 2022 Matan Atzmon, Koki Nagano, Sanja Fidler, Sameh Khamis, Yaron Lipman

A natural way to incorporate symmetries in shape space learning is to ask that the mapping to the shape space (encoder) and mapping from the shape space (decoder) are equivariant to the relevant symmetries.

Augmenting Implicit Neural Shape Representations with Explicit Deformation Fields

no code implementations19 Aug 2021 Matan Atzmon, David Novotny, Andrea Vedaldi, Yaron Lipman

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.


Isometric Autoencoders

no code implementations16 Jun 2020 Amos Gropp, Matan Atzmon, Yaron Lipman

Two sources of bad generalization are: extrinsic, where the learned manifold possesses extraneous parts that are far from the data; and intrinsic, where the encoder and decoder introduce arbitrary distortion in the low dimensional parameterization.

Decoder Dimensionality Reduction

SALD: Sign Agnostic Learning with Derivatives

1 code implementation ICLR 2021 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 graphics applications.

regression valid

Implicit Geometric Regularization for Learning Shapes

4 code implementations ICML 2020 Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, Yaron Lipman

Representing shapes as level sets of neural networks has been recently proved to be useful for different shape analysis and reconstruction tasks.

SAL: Sign Agnostic Learning of Shapes from Raw Data

1 code implementation CVPR 2020 Matan Atzmon, Yaron Lipman

Recently, neural networks have been used as implicit representations for surface reconstruction, modelling, learning, and generation.

Surface Reconstruction

Controlling Neural Level Sets

2 code implementations NeurIPS 2019 Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, Yaron Lipman

In turn, the sample network can be used to incorporate the level set samples into a loss function of interest.

Surface Reconstruction

Point Convolutional Neural Networks by Extension Operators

1 code implementation27 Mar 2018 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.

3D Part Segmentation 3D Point Cloud Classification +2

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