58,660 research outputs found
A Robust and Efficient Method for Solving Point Distance Problems by Homotopy
The goal of Point Distance Solving Problems is to find 2D or 3D placements of
points knowing distances between some pairs of points. The common guideline is
to solve them by a numerical iterative method (\emph{e.g.} Newton-Raphson
method). A sole solution is obtained whereas many exist. However the number of
solutions can be exponential and methods should provide solutions close to a
sketch drawn by the user.Geometric reasoning can help to simplify the
underlying system of equations by changing a few equations and triangularizing
it.This triangularization is a geometric construction of solutions, called
construction plan. We aim at finding several solutions close to the sketch on a
one-dimensional path defined by a global parameter-homotopy using a
construction plan. Some numerical instabilities may be encountered due to
specific geometric configurations. We address this problem by changing
on-the-fly the construction plan.Numerical results show that this hybrid method
is efficient and robust
Eigenvector Synchronization, Graph Rigidity and the Molecule Problem
The graph realization problem has received a great deal of attention in
recent years, due to its importance in applications such as wireless sensor
networks and structural biology. In this paper, we extend on previous work and
propose the 3D-ASAP algorithm, for the graph realization problem in
, given a sparse and noisy set of distance measurements. 3D-ASAP
is a divide and conquer, non-incremental and non-iterative algorithm, which
integrates local distance information into a global structure determination.
Our approach starts with identifying, for every node, a subgraph of its 1-hop
neighborhood graph, which can be accurately embedded in its own coordinate
system. In the noise-free case, the computed coordinates of the sensors in each
patch must agree with their global positioning up to some unknown rigid motion,
that is, up to translation, rotation and possibly reflection. In other words,
to every patch there corresponds an element of the Euclidean group Euc(3) of
rigid transformations in , and the goal is to estimate the group
elements that will properly align all the patches in a globally consistent way.
Furthermore, 3D-ASAP successfully incorporates information specific to the
molecule problem in structural biology, in particular information on known
substructures and their orientation. In addition, we also propose 3D-SP-ASAP, a
faster version of 3D-ASAP, which uses a spectral partitioning algorithm as a
preprocessing step for dividing the initial graph into smaller subgraphs. Our
extensive numerical simulations show that 3D-ASAP and 3D-SP-ASAP are very
robust to high levels of noise in the measured distances and to sparse
connectivity in the measurement graph, and compare favorably to similar
state-of-the art localization algorithms.Comment: 49 pages, 8 figure
Combining constructive and equational geometric constraint solving techniques
In the past few years, there has been a strong trend towards
developing parametric, computer aided design systems based on
geometric constraint solving. An efective way to capture the design
intent in these systems is to define relationships between geometric
and technological variables.
In general, geometric constraint solving including functional
relationships requires a general approach and appropiate techniques toachieve the expected functional capabilities.
This work reports on a hybrid method which combines two geometric
constraint solving techniques: Constructive and equational.
The hybrid solver has the capability of managing functional
relationships between dimension variables and variables representing
conditions external to the geometric problem.
The hybrid solver is described as a rewriting system and is shown to
be correct.Postprint (published version
Robust and Efficient Recovery of Rigid Motion from Subspace Constraints Solved using Recursive Identification of Nonlinear Implicit Systems
The problem of estimating rigid motion from projections may be characterized using a nonlinear dynamical system, composed of the rigid motion transformation and the perspective map. The time derivative of the output of such a system, which is also called the "motion field", is bilinear in the motion parameters, and may be used to specify a subspace constraint on either the direction of translation or the inverse depth of the observed points. Estimating motion may then be formulated as an optimization task constrained on such a subspace. Heeger and Jepson [5], who first introduced this constraint, solve the optimization task using an extensive search over the possible directions of translation.
We reformulate the optimization problem in a systems theoretic framework as the the identification of a dynamic system in exterior differential form with parameters on a differentiable manifold, and use techniques which pertain to nonlinear estimation and identification theory to perform the optimization task in a principled manner. The general technique for addressing such identification problems [14] has been used successfully in addressing other problems in computational vision [13, 12].
The application of the general method [14] results in a recursive and pseudo-optimal solution of the motion problem, which has robustness properties far superior to other existing techniques we have implemented.
By releasing the constraint that the visible points lie in front of the observer, we may explain some psychophysical effects on the nonrigid percept of rigidly moving shapes.
Experiments on real and synthetic image sequences show very promising results in terms of robustness, accuracy and computational efficiency
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