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We consider the problem of localizing a novel image in a large 3D model, given that the
gravitational vector is known. In principle, this is just an instance of camera pose estimation …

Stochastic convex optimization is a basic and well studied primitive in machine learning. It is
well known that convex and Lipschitz functions can be minimized efficiently using Stochastic …

We propose a real-time, robust to outliers and accurate solution to the Perspective-n-Point
(PnP) problem. The main advantages of our solution are twofold: first, it in-tegrates the …

This paper introduces a new algorithmic technique for solving certain problems in geometric
computer vision. The main novelty of the method is a branch-and-bound search over rotation …

This paper presents a new framework for solving geometric structure and motion problems
based on the L infin-norm. Instead of using the common sum-of-squares cost function, that …

As two important types of generalized convex functions, pseudoconvex and quasiconvex
functions appear in many practical optimization problems. The lack of convexity poses some …

In this paper we tackle the problem of learning Structure-from-Motion (SfM) through the use
of graph attention networks. SfM is a classic computer vision problem that is solved though …

The question to what extent particle induced transients in combinational parts of a circuit
propagate into memory elements is addressed in this paper An experimental method is …

We consider the problem of localizing a novel image in a large 3D model. In principle, this is
just an instance of camera pose estimation, but the scale introduces some challenging …