3,480 research outputs found
Robust and Fast 3D Scan Alignment using Mutual Information
This paper presents a mutual information (MI) based algorithm for the
estimation of full 6-degree-of-freedom (DOF) rigid body transformation between
two overlapping point clouds. We first divide the scene into a 3D voxel grid
and define simple to compute features for each voxel in the scan. The two scans
that need to be aligned are considered as a collection of these features and
the MI between these voxelized features is maximized to obtain the correct
alignment of scans. We have implemented our method with various simple point
cloud features (such as number of points in voxel, variance of z-height in
voxel) and compared the performance of the proposed method with existing
point-to-point and point-to- distribution registration methods. We show that
our approach has an efficient and fast parallel implementation on GPU, and
evaluate the robustness and speed of the proposed algorithm on two real-world
datasets which have variety of dynamic scenes from different environments
LASS: a simple assignment model with Laplacian smoothing
We consider the problem of learning soft assignments of items to
categories given two sources of information: an item-category similarity
matrix, which encourages items to be assigned to categories they are similar to
(and to not be assigned to categories they are dissimilar to), and an item-item
similarity matrix, which encourages similar items to have similar assignments.
We propose a simple quadratic programming model that captures this intuition.
We give necessary conditions for its solution to be unique, define an
out-of-sample mapping, and derive a simple, effective training algorithm based
on the alternating direction method of multipliers. The model predicts
reasonable assignments from even a few similarity values, and can be seen as a
generalization of semisupervised learning. It is particularly useful when items
naturally belong to multiple categories, as for example when annotating
documents with keywords or pictures with tags, with partially tagged items, or
when the categories have complex interrelations (e.g. hierarchical) that are
unknown.Comment: 20 pages, 4 figures. A shorter version appears in AAAI 201
A Sampling Strategy for High-Dimensional Spaces Applied to Free-Form Gravitational Lensing
We present a novel proposal strategy for the Metropolis-Hastings algorithm
designed to efficiently sample general convex polytopes in 100 or more
dimensions. This improves upon previous sampling strategies used for free-form
reconstruction of gravitational lenses, but is general enough to be applied to
other fields. We have written a parallel implementation within the lens
modeling framework GLASS. Testing shows that we are able to produce uniform
uncorrelated random samples which are necessary for exploring the degeneracies
inherent in lens reconstruction.Comment: 10 pages, 9 figures. Accepted for publication in MNRA
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