2,071 research outputs found
Integrating data from 3D CAD and 3D cameras for Real-Time Modeling
In a reversal of historic trends, the capital facilities industry is expressing an increasing desire for automation of equipment and construction processes. Simultaneously, the industry has become conscious that higher levels of interoperability are a key towards higher productivity and safer projects. In complex, dynamic, and rapidly changing three-dimensional (3D) environments such as facilities sites, cutting-edge 3D sensing technologies and processing algorithms are one area of development that can dramatically impact those projects factors. New 3D technologies are now being developed, with among them 3D camera. The main focus here is an investigation of the feasibility of rapidly combining and comparing – integrating – 3D sensed data (from a 3D camera) and 3D CAD data. Such a capability could improve construction quality assessment, facility aging assessment, as well as rapid environment reconstruction and construction automation. Some preliminary results are presented here. They deal with the challenge of fusing sensed and CAD data that are completely different in nature
Self-improving Algorithms for Coordinate-wise Maxima
Computing the coordinate-wise maxima of a planar point set is a classic and
well-studied problem in computational geometry. We give an algorithm for this
problem in the \emph{self-improving setting}. We have (unknown) independent
distributions \cD_1, \cD_2, ..., \cD_n of planar points. An input pointset
is generated by taking an independent sample from
each \cD_i, so the input distribution \cD is the product \prod_i \cD_i. A
self-improving algorithm repeatedly gets input sets from the distribution \cD
(which is \emph{a priori} unknown) and tries to optimize its running time for
\cD. Our algorithm uses the first few inputs to learn salient features of the
distribution, and then becomes an optimal algorithm for distribution \cD. Let
\OPT_\cD denote the expected depth of an \emph{optimal} linear comparison
tree computing the maxima for distribution \cD. Our algorithm eventually has
an expected running time of O(\text{OPT}_\cD + n), even though it did not
know \cD to begin with.
Our result requires new tools to understand linear comparison trees for
computing maxima. We show how to convert general linear comparison trees to
very restricted versions, which can then be related to the running time of our
algorithm. An interesting feature of our algorithm is an interleaved search,
where the algorithm tries to determine the likeliest point to be maximal with
minimal computation. This allows the running time to be truly optimal for the
distribution \cD.Comment: To appear in Symposium of Computational Geometry 2012 (17 pages, 2
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Succinct Representations for Abstract Interpretation
Abstract interpretation techniques can be made more precise by distinguishing
paths inside loops, at the expense of possibly exponential complexity.
SMT-solving techniques and sparse representations of paths and sets of paths
avoid this pitfall. We improve previously proposed techniques for guided static
analysis and the generation of disjunctive invariants by combining them with
techniques for succinct representations of paths and symbolic representations
for transitions based on static single assignment. Because of the
non-monotonicity of the results of abstract interpretation with widening
operators, it is difficult to conclude that some abstraction is more precise
than another based on theoretical local precision results. We thus conducted
extensive comparisons between our new techniques and previous ones, on a
variety of open-source packages.Comment: Static analysis symposium (SAS), Deauville : France (2012
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