Search Results for author: Gautam Pai

Found 12 papers, 2 papers with code

Optimal Transport on the Lie Group of Roto-translations

no code implementations23 Feb 2024 Daan Bon, Gautam Pai, Gijs Bellaard, Olga Mula, Remco Duits

We develop a Sinkhorn like algorithm that can be efficiently implemented using fast and accurate distance approximations of the Lie group and GPU-friendly group convolutions.

Translation

Analysis of (sub-)Riemannian PDE-G-CNNs

no code implementations3 Oct 2022 Gijs Bellaard, Daan L. J. Bon, Gautam Pai, Bart M. N. Smets, Remco Duits

Typically, G-CNNs have the advantage over CNNs that they do not waste network capacity on training symmetries that should have been hard-coded in the network.

Implicit field supervision for robust non-rigid shape matching

1 code implementation15 Mar 2022 Ramana Sundararaman, Gautam Pai, Maks Ovsjanikov

Establishing a correspondence between two non-rigidly deforming shapes is one of the most fundamental problems in visual computing.

Data Augmentation

DPFM: Deep Partial Functional Maps

1 code implementation19 Oct 2021 Souhaib Attaiki, Gautam Pai, Maks Ovsjanikov

We consider the problem of computing dense correspondences between non-rigid shapes with potentially significant partiality.

Fast Sinkhorn Filters: Using Matrix Scaling for Non-Rigid Shape Correspondence With Functional Maps

no code implementations CVPR 2021 Gautam Pai, Jing Ren, Simone Melzi, Peter Wonka, Maks Ovsjanikov

In this paper, we provide a theoretical foundation for pointwise map recovery from functional maps and highlight its relation to a range of shape correspondence methods based on spectral alignment.

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.

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.

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.

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

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