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.
1 code implementation • 28 Apr 2024 • Navve Wasserman, Noam Rotstein, Roy Ganz, Ron Kimmel
We address this by leveraging the insight that removing objects (Inpaint) is significantly simpler than its inverse process of adding them (Paint), attributed to the utilization of segmentation mask datasets alongside inpainting models that inpaint within these masks.
no code implementations • 2 Apr 2024 • Yaniv Wolf, Amit Bracha, Ron Kimmel
We propose a novel approach for surface reconstruction from Gaussian splatting models.
1 code implementation • 23 Oct 2023 • Amit Bracha, Thomas Dagès, Ron Kimmel
Our study of functional maps led us to a novel method that establishes direct correspondence between partial and full shapes through feature matching bypassing the need for functional map intermediate spaces.
1 code implementation • 28 May 2023 • Noam Rotstein, David Bensaid, Shaked Brody, Roy Ganz, Ron Kimmel
Our proposed method, FuseCap, fuses the outputs of such vision experts with the original captions using a large language model (LLM), yielding comprehensive image descriptions.
Ranked #1 on Image Captioning on COCO Captions (CLIPScore metric)
no code implementations • 6 Mar 2023 • Roy Velich, Ron Kimmel
We evaluate our models qualitatively and quantitatively and propose a benchmark dataset to evaluate approximation models of differential invariants of planar curves.
no code implementations • 7 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).
no code implementations • 7 Jun 2022 • Oshri Halimi, Fabian Prada, Tuur Stuyck, Donglai Xiang, Timur Bagautdinov, He Wen, Ron Kimmel, Takaaki Shiratori, Chenglei Wu, Yaser Sheikh
Here, we propose an end-to-end pipeline for building drivable representations for clothing.
no code implementations • 18 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.
no code implementations • 11 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.
no code implementations • 15 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.
no code implementations • 10 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.
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.
no code implementations • 15 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.
no code implementations • 10 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 $Q$.
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.
no code implementations • 23 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.
no code implementations • 19 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.
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.
no code implementations • 24 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.
1 code implementation • 27 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.
no code implementations • 30 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.
no code implementations • 20 Mar 2019 • Dmitry Kuznichov, Alon Zvirin, Yaron Honen, Ron Kimmel
Deep learning techniques involving image processing and data analysis are constantly evolving.
no code implementations • 19 Mar 2019 • Moshe Lichtenstein, Gautam Pai, Ron Kimmel
A deep learning approach to numerically approximate the solution to the Eikonal equation is introduced.
1 code implementation • 25 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.
no code implementations • 19 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.
no code implementations • 18 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.
1 code implementation • 6 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.
no code implementations • 24 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
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.
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.
no code implementations • 16 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.
1 code implementation • 18 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.
1 code implementation • 25 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.
no code implementations • 7 Jul 2017 • Yoni Choukroun, Gautam Pai, Ron Kimmel
Here, we incorporate the order of vertices into an operator that defines a novel spectral domain.
no code implementations • 4 May 2017 • Ran Breuer, Ron Kimmel
Facial expressions play a significant role in human communication and behavior.
2 code implementations • ICCV 2017 • Matan Sela, Elad Richardson, Ron Kimmel
It has been recently shown that neural networks can recover the geometric structure of a face from a single given image.
no code implementations • 6 Dec 2016 • Ron Slossberg, Aaron Wetzler, Ron Kimmel
Stereo reconstruction from rectified images has recently been revisited within the context of deep learning.
no code implementations • 23 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.
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.
no code implementations • 7 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.
no code implementations • 22 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.
no code implementations • 22 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.
no code implementations • 18 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.
no code implementations • 14 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.
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.
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.
no code implementations • 21 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.
no code implementations • CVPR 2015 • Roy Or - El, Guy Rosman, Aaron Wetzler, Ron Kimmel, Alfred M. Bruckstein
The popularity of low-cost RGB-D scanners is increasing on a daily basis.
no code implementations • 15 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.
no code implementations • 29 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.
no code implementations • 18 Nov 2013 • Alon Shtern, Ron Kimmel
An important operation in geometry processing is finding the correspondences between pairs of shapes.