no code implementations • 4 Feb 2024 • Snir Hordan, Tal Amir, Nadav Dym
Finally, we show that a simple modification of this PPGN architecture can be used to obtain a universal equivariant architecture that can approximate all continuous equivariant functions uniformly.
no code implementations • 31 Jan 2023 • Snir Hordan, Tal Amir, Steven J. Gortler, Nadav Dym
Neural networks for point clouds, which respect their natural invariance to permutation and rigid motion, have enjoyed recent success in modeling geometric phenomena, from molecular dynamics to recommender systems.
no code implementations • 18 Jul 2022 • Tal Amir, Shahar Kovalsky, Nadav Dym
Our relaxation enjoys several theoretical and practical advantages: Theoretically, we prove that our method provides a $\sqrt{2}$-factor approximation to the Robust Procrustes problem, and that, under appropriate assumptions, it exactly recovers the true rigid motion from point correspondences contaminated by outliers.
2 code implementations • 18 May 2020 • Tal Amir, Ronen Basri, Boaz Nadler
We present a new approach to solve the sparse approximation or best subset selection problem, namely find a $k$-sparse vector ${\bf x}\in\mathbb{R}^d$ that minimizes the $\ell_2$ residual $\lVert A{\bf x}-{\bf y} \rVert_2$.
no code implementations • CVPR 2017 • Soumyadip Sengupta, Tal Amir, Meirav Galun, Tom Goldstein, David W. Jacobs, Amit Singer, Ronen Basri
We show that in general, with the selection of proper scale factors, a matrix formed by stacking fundamental matrices between pairs of images has rank 6.
no code implementations • ICCV 2015 • Meirav Galun, Tal Amir, Tal Hassner, Ronen Basri, Yaron Lipman
This paper focuses on the challenging problem of finding correspondences once approximate epipolar constraints are given.
no code implementations • 10 Jun 2015 • Meirav Galun, Tal Amir, Tal Hassner, Ronen Basri, Yaron Lipman
This paper focuses on the challenging problem of finding correspondences once approximate epipolar constraints are given.