Rotation Averaging and Strong Duality

In this paper we explore the role of duality principles within the problem of rotation averaging, a fundamental task in a wide range of computer vision applications. In its conventional form, rotation averaging is stated as a minimization over multiple rotation constraints. As these constraints are non-convex, this problem is generally considered challenging to solve globally. We show how to circumvent this difficulty through the use of Lagrangian duality. While such an approach is well-known it is normally not guaranteed to provide a tight relaxation. Based on spectral graph theory, we analytically prove that in many cases there is no duality gap unless the noise levels are severe. This allows us to obtain certifiably global solutions to a class of important non-convex problems in polynomial time. We also propose an efficient, scalable algorithm that out-performs general purpose numerical solvers and is able to handle the large problem instances commonly occurring in structure from motion settings. The potential of this proposed method is demonstrated on a number of different problems, consisting of both synthetic and real-world data

Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging

Under mild conditions on the noise level of the measurements, rotation averaging satisfies strong duality, which enables global solutions to be obtained via semidefinite programming (SDP) relaxation. However, generic solvers for SDP are rather slow in practice, even on rotation averaging instances of moderate size, thus developing specialised algorithms is vital. In this paper, we present a fast algorithm that achieves global optimality called rotation coordinate descent (RCD). Unlike block coordinate descent (BCD) which solves SDP by updating the semidefinite matrix in a row-by-row fashion, RCD directly maintains and updates all valid rotations throughout the iterations. This obviates the need to store a large dense semidefinite matrix. We mathematically prove the convergence of our algorithm and empirically show its superior efficiency over state-of-the-art global methods on a variety of problem configurations. Maintaining valid rotations also facilitates incorporating local optimisation routines for further speed-ups. Moreover, our algorithm is simple to implement; see supplementary material for a demonstration program.Comment: Accepted to CVPR 2021 as an oral presentatio

Strings as Solitons & Black Holes as Strings

Supersymmetric closed string theories contain an infinite tower of BPS-saturated, oscillating, macroscopic strings in the perturbative spectrum. When these theories have dual formulations, this tower of states must exist nonperturbatively as solitons in the dual theories. We present a general class of exact solutions of low-energy supergravity that corresponds to all these states. After dimensional reduction they can be interpreted as supersymmetric black holes with a degeneracy related to the degeneracy of the string states. {}For example, in four dimensions we obtain a point-like solution which is asymptotic to a stationary, rotating, electrically-charged black hole with Regge-bounded angular momentum and with the usual ring-singularity replaced by a string source. This further supports the idea that the entropy of supersymmetric black holes can be understood in terms of counting of string states. We also discuss some applications of these solutions to string duality.Comment: 52 pages, harvmac (b