59 citations · 62 across the 9 of their papers we have counts for
10 papers
Analysis of (sub-)Riemannian PDE-G-CNNs
Gijs Bellaard, Daan L. J. Bon, Gautam Pai +2
Group equivariant convolutional neural networks (G-CNNs) have been successfully applied in geometric deep learning. Typically, G-CNNs have the advantage over CNNs that they do not…
Geodesic Tracking via New Data-driven Connections of Cartan Type for Vascular Tree Tracking
Nicky van den Berg, Bart Smets, Gautam Pai +2
We introduce a data-driven version of the plus Cartan connection on the homogeneous space of 2D positions and orientations. We formulate a theorem that describes all…
Implicit field supervision for robust non-rigid shape matching
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. Existing methods often show weak resilience when…
DPFM: Deep Partial Functional Maps
Souhaib Attaiki, Gautam Pai, Maks Ovsjanikov
We consider the problem of computing dense correspondences between non-rigid shapes with potentially significant partiality. Existing formulations tackle this problem through heavy…
A modified Allen-Cahn model for pattern synthesis on surfaces
Lorina Dascal, Gautam Pai, Ron Kimmel
We propose an extension of the Allen-Cahn model for pattern synthesis on two dimensional curved surfaces. This model is based on a single PDE and it offers improved ability of cont…
Bilateral Operators for Functional Maps
Gautam Pai, Mor Joseph-Rivlin, Ron Kimmel
A majority of shape correspondence frameworks are based on devising pointwise and pairwise constraints on the correspondence map. The functional maps framework allows for formulati…
Deep Eikonal Solvers
Moshe Lichtenstein, Gautam Pai, Ron Kimmel
A deep learning approach to numerically approximate the solution to the Eikonal equation is introduced. The proposed method is built on the fast marching scheme which comprises of…
DIMAL: Deep Isometric Manifold Learning Using Sparse Geodesic Sampling
Gautam Pai, Ronen Talmon, Alex Bronstein +1
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. We…
Sparse Approximation of 3D Meshes using the Spectral Geometry of the Hamiltonian Operator
Yoni Choukroun, Gautam Pai, Ron Kimmel
The discrete Laplace operator is ubiquitous in spectral shape analysis, since its eigenfunctions are provably optimal in representing smooth functions defined on the surface of the…
Learning Invariant Representations Of Planar Curves
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. We leverage…