Search Results for author: Ron Kimmel

Found 47 papers, 9 papers with code

Towards Precise Completion of Deformable Shapes

1 code implementation ECCV 2020 Oshri Halimi, Ido Imanuel, Or Litany, Giovanni Trappolini, Emanuele Rodolà, Leonidas Guibas, Ron Kimmel

Here, we claim that observing part of an object which was previously acquired as a whole, one could deal with both partial matching and shape completion in a holistic manner.

Partial Shape Similarity via Alignment of Multi-Metric Hamiltonian Spectra

no code implementations7 Jul 2022 David Bensaïd, Amit Bracha, Ron Kimmel

Matching similar regions is formulated as the alignment of the spectra of operators closely related to the Laplace-Beltrami operator (LBO).

Elastica Models for Color Image Regularization

no code implementations18 Mar 2022 Hao liu, Xue-Cheng Tai, Ron Kimmel, Roland Glowinski

Recently, the authors proposed a color elastica model, which minimizes both the surface area and elastica of the image manifold.

Deep Signatures -- Learning Invariants of Planar Curves

no code implementations11 Feb 2022 Roy Velich, Ron Kimmel

We compare the proposed schemes to alternative state-of-the-art axiomatic constructions of group invariant arc-lengths and curvatures.

Depth Refinement for Improved Stereo Reconstruction

no code implementations15 Dec 2021 Amit Bracha, Noam Rotstein, David Bensaïd, Ron Slossberg, Ron Kimmel

To mitigate this quadratic relation, we propose a simple but effective method that uses a refinement network for depth estimation.

Autonomous Driving Depth Estimation +1

Unsupervised High-Fidelity Facial Texture Generation and Reconstruction

no code implementations10 Oct 2021 Ron Slossberg, Ibrahim Jubran, Ron Kimmel

In this paper, we propose a novel unified pipeline for both tasks, generation of both geometry and texture, and recovery of high-fidelity texture.

Image Generation Texture Synthesis +1

Multimodal Colored Point Cloud to Image Alignment

1 code implementation CVPR 2022 Noam Rotstein, Amit Bracha, Ron Kimmel

To overcome this difficulty, we consider a differential optimization method that aligns a colored point cloud with a given color image through iterative geometric and color matching.

U-mesh: Human Correspondence Matching with Mesh Convolutional Networks

no code implementations15 Aug 2021 Benjamin Groisser, Alon Wolf, Ron Kimmel

Modeling correspondence as Euclidean proximity enables efficient optimization, both for network training and for the next step of the algorithm.

Provably Approximated ICP

no code implementations10 Jan 2021 Ibrahim Jubran, Alaa Maalouf, Ron Kimmel, Dan Feldman

A harder version is the \emph{registration problem}, where the correspondence is unknown, and the minimum is also over all possible correspondence functions from $P$ to $Q$.

Provably Approximated Point Cloud Registration

no code implementations ICCV 2021 Ibrahim Jubran, Alaa Maalouf, Ron Kimmel, Dan Feldman

A harder version is the registration problem, where the correspondence is unknown, and the minimum is also over all possible correspondence functions from P to Q. Algorithms such as the Iterative Closest Point (ICP) and its variants were suggested for these problems, but none yield a provable non-trivial approximation for the global optimum.

Point Cloud Registration

Abiotic Stress Prediction from RGB-T Images of Banana Plantlets

no code implementations23 Nov 2020 Sagi Levanon, Oshry Markovich, Itamar Gozlan, Ortal Bakhshian, Alon Zvirin, Yaron Honen, Ron Kimmel

Prediction of stress conditions is important for monitoring plant growth stages, disease detection, and assessment of crop yields.

A Color Elastica Model for Vector-Valued Image Regularization

no code implementations19 Aug 2020 Hao Liu, Xue-Cheng Tai, Ron Kimmel, Roland Glowinski

Here, we introduce an addition to the Polyakov action for color images that minimizes the color manifold curvature.

LIMP: Learning Latent Shape Representations with Metric Preservation Priors

1 code implementation ECCV 2020 Luca Cosmo, Antonio Norelli, Oshri Halimi, Ron Kimmel, Emanuele Rodolà

In this paper, we advocate the adoption of metric preservation as a powerful prior for learning latent representations of deformable 3D shapes.

Style Transfer

Do We Need Depth in State-Of-The-Art Face Authentication?

no code implementations24 Mar 2020 Amir Livne, Alex Bronstein, Ron Kimmel, Ziv Aviv, Shahaf Grofit

The raw face stereo images along with the location in the image from which the face is extracted allow the proposed CNN to improve the recognition task while avoiding the need to explicitly handle the geometric structure of the face.

Face Recognition

The Whole Is Greater Than the Sum of Its Nonrigid Parts

1 code implementation27 Jan 2020 Oshri Halimi, Ido Imanuel, Or Litany, Giovanni Trappolini, Emanuele Rodolà, Leonidas Guibas, Ron Kimmel

Here, we claim that observing part of an object which was previously acquired as a whole, one could deal with both partial matching and shape completion in a holistic manner.

Bilateral Operators for Functional Maps

no code implementations30 Jul 2019 Gautam Pai, Mor Joseph-Rivlin, Ron Kimmel

In this paper, we develop a functional map framework for the shape correspondence problem by constructing pairwise constraints using point-wise descriptors.

Deep Eikonal Solvers

no code implementations19 Mar 2019 Moshe Lichtenstein, Gautam Pai, Ron Kimmel

A deep learning approach to numerically approximate the solution to the Eikonal equation is introduced.

Learning to Optimize Multigrid PDE Solvers

1 code implementation25 Feb 2019 Daniel Greenfeld, Meirav Galun, Ron Kimmel, Irad Yavneh, Ronen Basri

Constructing fast numerical solvers for partial differential equations (PDEs) is crucial for many scientific disciplines.

Synthesizing facial photometries and corresponding geometries using generative adversarial networks

no code implementations19 Jan 2019 Gil Shamai, Ron Slossberg, Ron Kimmel

We circumvent the parametrization issue by imposing a global mapping from our data to the unit rectangle.

Momen(e)t: Flavor the Moments in Learning to Classify Shapes

no code implementations18 Dec 2018 Mor Joseph-Rivlin, Alon Zvirin, Ron Kimmel

A fundamental question in learning to classify 3D shapes is how to treat the data in a way that would allow us to construct efficient and accurate geometric processing and analysis procedures.

Self-supervised Learning of Dense Shape Correspondence

1 code implementation6 Dec 2018 Oshri Halimi, Or Litany, Emanuele Rodolà, Alex Bronstein, Ron Kimmel

The resulting learning model is class-agnostic, and is able to leverage any type of deformable geometric data for the training phase.

Self-Supervised Learning

High Quality Facial Surface and Texture Synthesis via Generative Adversarial Networks

no code implementations24 Aug 2018 Ron Slossberg, Gil Shamai, Ron Kimmel

A GAN is employed in order to imitate the space of parametrized human textures, while corresponding facial geometries are generated by learning the best 3DMM coefficients for each texture.

Computational Geometry

Specular-to-Diffuse Translation for Multi-View Reconstruction

no code implementations ECCV 2018 Shihao Wu, Hui Huang, Tiziano Portenier, Matan Sela, Danny Cohen-Or, Ron Kimmel, Matthias Zwicker

To alleviate this restriction, we introduce S2Dnet, a generative adversarial network for transferring multiple views of objects with specular reflection into diffuse ones, so that multi-view reconstruction methods can be applied more effectively.

3D Reconstruction Lighting Estimation +2

Parametric Manifold Learning Via Sparse Multidimensional Scaling

no code implementations ICLR 2018 Gautam Pai, Ronen Talmon, Ron Kimmel

We propose a metric-learning framework for computing distance-preserving maps that generate low-dimensional embeddings for a certain class of manifolds.

Metric Learning

DIMAL: Deep Isometric Manifold Learning Using Sparse Geodesic Sampling

no code implementations16 Nov 2017 Gautam Pai, Ronen Talmon, Alex Bronstein, Ron Kimmel

This paper explores a fully unsupervised deep learning approach for computing distance-preserving maps that generate low-dimensional embeddings for a certain class of manifolds.

CoBe -- Coded Beacons for Localization, Object Tracking, and SLAM Augmentation

1 code implementation18 Aug 2017 Roman Rabinovich, Ibrahim Jubran, Aaron Wetzler, Ron Kimmel

This paper presents a novel beacon light coding protocol, which enables fast and accurate identification of the beacons in an image.

Object Tracking Simultaneous Localization and Mapping

Efficient Deformable Shape Correspondence via Kernel Matching

1 code implementation25 Jul 2017 Zorah Lähner, Matthias Vestner, Amit Boyarski, Or Litany, Ron Slossberg, Tal Remez, Emanuele Rodolà, Alex Bronstein, Michael Bronstein, Ron Kimmel, Daniel Cremers

We present a method to match three dimensional shapes under non-isometric deformations, topology changes and partiality.

Sparse Approximation of 3D Meshes using the Spectral Geometry of the Hamiltonian Operator

no code implementations7 Jul 2017 Yoni Choukroun, Gautam Pai, Ron Kimmel

Here, we incorporate the order of vertices into an operator that defines a novel spectral domain.

A Deep Learning Perspective on the Origin of Facial Expressions

no code implementations4 May 2017 Ran Breuer, Ron Kimmel

Facial expressions play a significant role in human communication and behavior.

Transfer Learning

Deep Stereo Matching with Dense CRF Priors

no code implementations6 Dec 2016 Ron Slossberg, Aaron Wetzler, Ron Kimmel

Stereo reconstruction from rectified images has recently been revisited within the context of deep learning.

Stereo Matching Stereo Matching Hand

Learning Invariant Representations Of Planar Curves

no code implementations23 Nov 2016 Gautam Pai, Aaron Wetzler, Ron Kimmel

We propose a metric learning framework for the construction of invariant geometric functions of planar curves for the Eucledian and Similarity group of transformations.

Metric Learning

Learning Detailed Face Reconstruction from a Single Image

no code implementations CVPR 2017 Elad Richardson, Matan Sela, Roy Or-El, Ron Kimmel

In contrast, we propose to leverage the power of convolutional neural networks to produce a highly detailed face reconstruction from a single image.

Face Reconstruction Object Recognition

Hamiltonian operator for spectral shape analysis

no code implementations7 Nov 2016 Yoni Choukroun, Alon Shtern, Alex Bronstein, Ron Kimmel

Many shape analysis methods treat the geometry of an object as a metric space that can be captured by the Laplace-Beltrami operator.

Randomized Independent Component Analysis

no code implementations22 Sep 2016 Matan Sela, Ron Kimmel

Independent component analysis (ICA) is a method for recovering statistically independent signals from observations of unknown linear combinations of the sources.

Customized Facial Constant Positive Air Pressure (CPAP) Masks

no code implementations22 Sep 2016 Matan Sela, Nadav Toledo, Yaron Honen, Ron Kimmel

The incompatibility is characterized by gaps between the mask and the face, which deteriorates the impermeability of the mask and leads to air leakage.

Consistent Discretization and Minimization of the L1 Norm on Manifolds

no code implementations18 Sep 2016 Alex Bronstein, Yoni Choukroun, Ron Kimmel, Matan Sela

The L1 norm has been tremendously popular in signal and image processing in the past two decades due to its sparsity-promoting properties.

3D Face Reconstruction by Learning from Synthetic Data

no code implementations14 Sep 2016 Elad Richardson, Matan Sela, Ron Kimmel

Fast and robust three-dimensional reconstruction of facial geometric structure from a single image is a challenging task with numerous applications.

3D Face Reconstruction

Classical Scaling Revisited

no code implementations ICCV 2015 Gil Shamai, Yonathan Aflalo, Michael Zibulevsky, Ron Kimmel

We present an efficient solver for Classical Scaling (a specific MDS model) by extending the distances measured from a subset of the points to the rest, while exploiting the smoothness property of the distance functions.

Real-Time Depth Refinement for Specular Objects

no code implementations CVPR 2016 Roy Or - El, Rom Hershkovitz, Aaron Wetzler, Guy Rosman, Alfred M. Bruckstein, Ron Kimmel

The introduction of consumer RGB-D scanners set off a major boost in 3D computer vision research.

Rule Of Thumb: Deep derotation for improved fingertip detection

no code implementations21 Jul 2015 Aaron Wetzler, Ron Slossberg, Ron Kimmel

We investigate a novel global orientation regression approach for articulated objects using a deep convolutional neural network.

Fingertip Detection General Classification +2

On the optimality of shape and data representation in the spectral domain

no code implementations15 Sep 2014 Yonathan Aflalo, Haim Brezis, Ron Kimmel

This novel pseudo-metric allows constructing an LBO by which a scale invariant eigenspace on the surface is defined.

Graph matching: relax or not?

no code implementations29 Jan 2014 Yonathan Aflalo, Alex Bronstein, Ron Kimmel

We consider the problem of exact and inexact matching of weighted undirected graphs, in which a bijective correspondence is sought to minimize a quadratic weight disagreement.

Graph Matching

On Nonrigid Shape Similarity and Correspondence

no code implementations18 Nov 2013 Alon Shtern, Ron Kimmel

An important operation in geometry processing is finding the correspondences between pairs of shapes.

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