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We present a simple and efficient method for refining maps or correspondences by iterative upsampling in the spectral domain that can be implemented in a few lines of code. Our main observation is that high quality maps can be obtained even…

Graphics · Computer Science 2019-09-13 Simone Melzi , Jing Ren , Emanuele Rodolà , Abhishek Sharma , Peter Wonka , Maks Ovsjanikov

Via numerical simulations and analytical calculations, depletion forces are studied in mixtures of small and big particles that interact via soft repulsive potentials. While big particles are spherical, small particles are nonspherical with…

Soft Condensed Matter · Physics 2024-09-13 Itay Azizi

The scalar-on-function regression model has become a popular analysis tool to explore the relationship between a scalar response and multiple functional predictors. Most of the existing approaches to estimate this model are based on the…

Methodology · Statistics 2022-03-11 Ufuk Beyaztas , Han Lin Shang

The aim of this study was to develop a model to accurately identify corresponding points between organ segmentations of different patients for radiotherapy applications. A model for simultaneous correspondence and interpolation estimation…

Computer Vision and Pattern Recognition · Computer Science 2023-09-26 Edward G. A. Henderson , Marcel van Herk , Andrew F. Green , Eliana M. Vasquez Osorio

In computer vision and graphics, various types of symmetries are extensively studied since symmetry present in objects is a fundamental cue for understanding the shape and the structure of objects. In this work, we detect the intrinsic…

Computer Vision and Pattern Recognition · Computer Science 2018-12-06 Rajendra Nagar , Shanmuganathan Raman

Spectral geometric methods have brought revolutionary changes to the field of geometry processing. Of particular interest is the study of the Laplacian spectrum as a compact, isometry and permutation-invariant representation of a shape.…

We consider functional linear regression models where functional outcomes are associated with scalar predictors by coefficient functions with shape constraints, such as monotonicity and convexity, that apply to sub-domains of interest. To…

Methodology · Statistics 2025-05-09 Kyunghee Han , Yeonjoo Park , Soo-Young Kim

The conjugate function method is an algorithm for numerical computation of conformal mappings for simply and multiply connected domains. In this paper, the conjugate function method is extended to cover conformal mappings between Riemannian…

Numerical Analysis · Mathematics 2024-04-22 Harri Hakula , Antti Rasila

The shape function f(k_+) describes Fermi motion effects in inclusive semi-leptonic decays such as B -> X_u+e+nu near the end-point of the lepton spectrum. We compute the leading one-loop corrections to the shape function f(k_+) in the…

High Energy Physics - Phenomenology · Physics 2011-02-01 U. Aglietti , G. Ricciardi

We consider the tasks of representing, analyzing and manipulating maps between shapes. We model maps as densities over the product manifold of the input shapes; these densities can be treated as scalar functions and therefore are…

Graphics · Computer Science 2019-01-10 Emanuele Rodolà , Zorah Lähner , Alex M. Bronstein , Michael M. Bronstein , Justin Solomon

We compare surface metrics for shape optimization problems with constraints, consisting mainly of partial differential equations (PDE), from a computational point of view. In particular, classical Laplace-Beltrami type based metrics are…

Optimization and Control · Mathematics 2021-04-12 Volker Schulz , Martin Siebenborn

Many shape analysis methods treat the geometry of an object as a metric space that can be captured by the Laplace-Beltrami operator. In this paper, we propose to adapt the classical Hamiltonian operator from quantum mechanics to the field…

Graphics · Computer Science 2017-06-27 Yoni Choukroun , Alon Shtern , Alex Bronstein , Ron Kimmel

In this paper we propose an approach for computing multiple high-quality near-isometric dense correspondences between a pair of 3D shapes. Our method is fully automatic and does not rely on user-provided landmarks or descriptors. This…

Graphics · Computer Science 2020-09-11 Jing Ren , Simone Melzi , Maks Ovsjanikov , Peter Wonka

An important operation in geometry processing is finding the correspondences between pairs of shapes. The Gromov-Hausdorff distance, a measure of dissimilarity between metric spaces, has been found to be highly useful for nonrigid shape…

Computer Vision and Pattern Recognition · Computer Science 2013-11-25 Alon Shtern , Ron Kimmel

We develop a perturbative approach for calculating, within the quasistatic approximation, the shift of surface resonances in response to a deformation of a dielectric volume. Our strategy is based on the conversion of the homogeneous system…

Mesoscale and Nanoscale Physics · Physics 2015-05-27 Daniel Grieser , Hannes Uecker , Svend-Age Biehs , Oliver Huth , Felix Rüting , Martin Holthaus

We analyze integral representation and $\Gamma$-convergence properties of functionals defined on \emph{piecewise rigid functions}, i.e., functions which are piecewise affine on a Caccioppoli partition where the derivative in each component…

Analysis of PDEs · Mathematics 2020-02-04 Manuel Friedrich , Francesco Solombrino

We study the functional linear regression model with a scalar response and a Hilbert space-valued predictor, a canonical example of an ill-posed inverse problem. We show that the functional partial least squares (PLS) estimator attains…

Statistics Theory · Mathematics 2025-05-08 Andrii Babii , Marine Carrasco , Idriss Tsafack

We study two possible prescriptions for AdS/CFT correspondence by means of functional integrals. The considerations are non-perturbative and reveal certain divergencies which turn out to be harmless, in the sense that reflection-positivity…

Mathematical Physics · Physics 2008-11-26 H. Gottschalk , H. Thaler

The question whether one can recover the shape of a geometric object from its Laplacian spectrum ('hear the shape of the drum') is a classical problem in spectral geometry with a broad range of implications and applications. While…

Computational Geometry · Computer Science 2020-09-09 Luca Cosmo , Mikhail Panine , Arianna Rampini , Maks Ovsjanikov , Michael M. Bronstein , Emanuele Rodolà

We present a novel learning-based approach for computing correspondences between non-rigid 3D shapes. Unlike previous methods that either require extensive training data or operate on handcrafted input descriptors and thus generalize poorly…

Machine Learning · Statistics 2020-04-01 Nicolas Donati , Abhishek Sharma , Maks Ovsjanikov