56 research outputs found
Multiview Registration via Graph Diffusion of Dual Quaternions.
Surface registration is a fundamental step in the reconstruction of three-dimensional objects. While there are several fast and reliable methods to align two surfaces, the tools available to align multiple surfaces are relatively limited. In this paper we propose a novel multiview registration algorithm that projects several pairwise alignments onto a common reference frame. The projection is performed by representing the motions as dual quaternions, an algebraic structure that is related to the group of 3D rigid transformations, and by performing a diffusion along the graph of adjacent (i.e., pairwise alignable) views. The approach allows for a completely generic topology with which the pair-wise motions are diffused. An extensive set of experiments shows that the proposed approach is both orders of magnitude faster than the state of the art, and more robust to extreme positional noise and outliers. The dramatic speedup of the approach allows it to be alternated with pairwise alignment resulting in a smoother energy profile, reducing the risk of getting stuck at local minima
Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC
We introduce Tempered Geodesic Markov Chain Monte Carlo (TG-MCMC) algorithm
for initializing pose graph optimization problems, arising in various scenarios
such as SFM (structure from motion) or SLAM (simultaneous localization and
mapping). TG-MCMC is first of its kind as it unites asymptotically global
non-convex optimization on the spherical manifold of quaternions with posterior
sampling, in order to provide both reliable initial poses and uncertainty
estimates that are informative about the quality of individual solutions. We
devise rigorous theoretical convergence guarantees for our method and
extensively evaluate it on synthetic and real benchmark datasets. Besides its
elegance in formulation and theory, we show that our method is robust to
missing data, noise and the estimated uncertainties capture intuitive
properties of the data.Comment: Published at NeurIPS 2018, 25 pages with supplement
A Survey on Dual-Quaternions
Over the past few years, the applications of dual-quaternions have not only
developed in many different directions but has also evolved in exciting ways in
several areas. As dual-quaternions offer an efficient and compact symbolic form
with unique mathematical properties. While dual-quaternions are now common
place in many aspects of research and implementation, such as, robotics and
engineering through to computer graphics and animation, there are still a large
number of avenues for exploration with huge potential benefits. This article is
the first to provide a comprehensive review of the dual-quaternion landscape.
In this survey, we present a review of dual-quaternion techniques and
applications developed over the years while providing insights into current and
future directions. The article starts with the definition of dual-quaternions,
their mathematical formulation, while explaining key aspects of importance
(e.g., compression and ambiguities). The literature review in this article is
divided into categories to help manage and visualize the application of
dual-quaternions for solving specific problems. A timeline illustrating key
methods is presented, explaining how dual-quaternion approaches have progressed
over the years. The most popular dual-quaternion methods are discussed with
regard to their impact in the literature, performance, computational cost and
their real-world results (compared to associated models). Finally, we indicate
the limitations of dual-quaternion methodologies and propose future research
directions.Comment: arXiv admin note: text overlap with arXiv:2303.1339
von Neumann type trace inequality for dual quaternion matrices
As a powerful tool to represent rigid body motion in 3D spaces, dual
quaternions have been successfully applied to robotics, 3D motion modelling and
control, and computer graphics. Due to the important applications in
multi-agent formation control, this paper addresses the concept of spectral
norm of dual quaternion matrices. We introduce a von Neumann type trace
inequality and a Hoffman-Wielandt type inequality for general dual quaternion
matrices, where the latter characterizes a simultaneous perturbation bound on
all singular values of a dual quaternion matrix. In particular, we also present
two variants of the above two inequalities expressed by eigenvalues of dual
quaternion Hermitian matrices. Our results are helpful for the further study of
dual quaternion matrix theory, algorithmic design, and applications
Synchronization Problems in Computer Vision
The goal of \u201csynchronization\u201d is to infer the unknown states of a network of nodes, where only the ratio (or difference) between pairs of states can be measured. Typically, states are represented by elements of a group, such as the Symmetric Group or the Special Euclidean Group. The former can represent local labels of a set of features, which refer to the multi-view matching application, whereas the latter can represent camera reference frames, in which case we are in the context of structure from motion, or local coordinates where 3D points are represented, in which case we are dealing with multiple point-set registration. A related problem is that of \u201cbearing-based network localization\u201d where each node is located at a fixed (unknown) position in 3-space and pairs of nodes can measure the direction of the line joining their locations. In this thesis we are interested in global techniques where all the measures are considered at once, as opposed to incremental approaches that grow a solution by adding pieces iteratively
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