Difference between revisions of "Projects:PointSetRigidRegistration"
From NAMIC Wiki
| Line 3: | Line 3: | ||
= Particle Filtering with Stochastic Dynamics for Point Set Registration = | = Particle Filtering with Stochastic Dynamics for Point Set Registration = | ||
| − | + | Typically, registration algorithms compute the transformations paramaters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem problem, in which the corresponding distribution can naturally be estimated using a particle filter. Moreover, we treat motion as a local variation in the pose parameters obatined from running a few iterations of the standard Iterative Closest Point (ICP) algorithm. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence as well as provide a dynamical model of uncertainity. | |
= Description = | = Description = | ||
Revision as of 02:48, 27 April 2008
Home < Projects:PointSetRigidRegistrationBack to NA-MIC Collaborations, Georgia Tech Algorithms
Particle Filtering with Stochastic Dynamics for Point Set Registration
Typically, registration algorithms compute the transformations paramaters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem problem, in which the corresponding distribution can naturally be estimated using a particle filter. Moreover, we treat motion as a local variation in the pose parameters obatined from running a few iterations of the standard Iterative Closest Point (ICP) algorithm. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence as well as provide a dynamical model of uncertainity.
Description
Algorithm
Project Status
Key Investigators
- Georgia Tech: Romeil Sandhu, Samuel Dambreville, Allen Tannenbaum
Publications
In press
- R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.
