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Found 7 papers, 1 papers with code
Weisfeiler Leman for Euclidean Equivariant Machine Learning
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.
Complete Neural Networks for Complete Euclidean Graphs
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.
Symmetrized Robust Procrustes: Constant-Factor Approximation and Exact Recovery
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.
The Trimmed Lasso: Sparse Recovery Guarantees and Practical Optimization by the Generalized Soft-Min Penalty
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 $\lVert A{\bf x}-{\bf y} \rVert_2$.
A New Rank Constraint on Multi-view Fundamental Matrices, and its Application to Camera Location Recovery
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.
Wide Baseline Stereo Matching With Convex Bounded Distortion Constraints
This paper focuses on the challenging problem of finding correspondences once approximate epipolar constraints are given.
Wide baseline stereo matching with convex bounded-distortion constraints
This paper focuses on the challenging problem of finding correspondences once approximate epipolar constraints are given.
