17,011 research outputs found
A Bayesian Approach to Manifold Topology Reconstruction
In this paper, we investigate the problem of statistical reconstruction of piecewise linear manifold topology. Given a noisy, probably undersampled point cloud from a one- or two-manifold, the algorithm reconstructs an approximated most likely mesh in a Bayesian sense from which the sample might have been taken. We incorporate statistical priors on the object geometry to improve the reconstruction quality if additional knowledge about the class of original shapes is available. The priors can be formulated analytically or learned from example geometry with known manifold tessellation. The statistical objective function is approximated by a linear programming / integer programming problem, for which a globally optimal solution is found. We apply the algorithm to a set of 2D and 3D reconstruction examples, demon-strating that a statistics-based manifold reconstruction is feasible, and still yields plausible results in situations where sampling conditions are violated
The Denoised, Deconvolved, and Decomposed Fermi -ray sky - An application of the DPO algorithm
We analyze the 6.5yr all-sky data from the Fermi LAT restricted to gamma-ray
photons with energies between 0.6-307.2GeV. Raw count maps show a superposition
of diffuse and point-like emission structures and are subject to shot noise and
instrumental artifacts. Using the D3PO inference algorithm, we model the
observed photon counts as the sum of a diffuse and a point-like photon flux,
convolved with the instrumental beam and subject to Poissonian shot noise. D3PO
performs a Bayesian inference in this setting without the use of spatial or
spectral templates;i.e., it removes the shot noise, deconvolves the
instrumental response, and yields estimates for the two flux components
separately. The non-parametric reconstruction uncovers the morphology of the
diffuse photon flux up to several hundred GeV. We present an all-sky spectral
index map for the diffuse component. We show that the diffuse gamma-ray flux
can be described phenomenologically by only two distinct components: a soft
component, presumably dominated by hadronic processes, tracing the dense, cold
interstellar medium and a hard component, presumably dominated by leptonic
interactions, following the hot and dilute medium and outflows such as the
Fermi bubbles. A comparison of the soft component with the Galactic dust
emission indicates that the dust-to-soft-gamma ratio in the interstellar medium
decreases with latitude. The spectrally hard component exists in a thick
Galactic disk and tends to flow out of the Galaxy at some locations.
Furthermore, we find the angular power spectrum of the diffuse flux to roughly
follow a power law with an index of 2.47 on large scales, independent of
energy. Our first catalog of source candidates includes 3106 candidates of
which we associate 1381(1897) with known sources from the 2nd(3rd) Fermi
catalog. We observe gamma-ray emission in the direction of a few galaxy
clusters hosting radio halos.Comment: re-submission after referee report (A&A); 17 pages, many colorful
figures, 4 tables; bug fixed, flux scale now consistent with Fermi, even
lower residual level, pDF -> 1DF source catalog, tentative detection of a few
clusters of galaxies, online material
http://www.mpa-garching.mpg.de/ift/fermi
Probabilistic Global Scale Estimation for MonoSLAM Based on Generic Object Detection
This paper proposes a novel method to estimate the global scale of a 3D
reconstructed model within a Kalman filtering-based monocular SLAM algorithm.
Our Bayesian framework integrates height priors over the detected objects
belonging to a set of broad predefined classes, based on recent advances in
fast generic object detection. Each observation is produced on single frames,
so that we do not need a data association process along video frames. This is
because we associate the height priors with the image region sizes at image
places where map features projections fall within the object detection regions.
We present very promising results of this approach obtained on several
experiments with different object classes.Comment: Int. Workshop on Visual Odometry, CVPR, (July 2017
A Bayesian Approach to Manifold Topology Reconstruction
In this paper, we investigate the problem of statistical reconstruction of piecewise linear manifold topology. Given a noisy, probably undersampled point cloud from a one- or two-manifold, the algorithm reconstructs an approximated most likely mesh in a Bayesian sense from which the sample might have been taken. We incorporate statistical priors on the object geometry to improve the reconstruction quality if additional knowledge about the class of original shapes is available. The priors can be formulated analytically or learned from example geometry with known manifold tessellation. The statistical objective function is approximated by a linear programming / integer programming problem, for which a globally optimal solution is found. We apply the algorithm to a set of 2D and 3D reconstruction examples, demon-strating that a statistics-based manifold reconstruction is feasible, and still yields plausible results in situations where sampling conditions are violated
Consistent ICP for the registration of sparse and inhomogeneous point clouds
In this paper, we derive a novel iterative closest point (ICP) technique that performs point cloud alignment in a robust and consistent way. Traditional ICP techniques minimize the point-to-point distances, which are successful when point clouds contain no noise or clutter and moreover are dense and more or less uniformly sampled. In the other case, it is better to employ point-to-plane or other metrics to locally approximate the surface of the objects. However, the point-to-plane metric does not yield a symmetric solution, i.e. the estimated transformation of point cloud p to point cloud q is not necessarily equal to the inverse transformation of point cloud q to point cloud p. In order to improve ICP, we will enforce such symmetry constraints as prior knowledge and make it also robust to noise and clutter. Experimental results show that our method is indeed much more consistent and accurate in presence of noise and clutter compared to existing ICP algorithms
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