23,243 research outputs found
Interactive design exploration for constrained meshes
In architectural design, surface shapes are commonly subject to geometric constraints imposed by material, fabrication or assembly. Rationalization algorithms can convert a freeform design into a form feasible for production, but often require design modifications that might not comply with the design intent. In addition, they only offer limited support for exploring alternative feasible shapes, due to the high complexity of the optimization algorithm. We address these shortcomings and present a computational framework for interactive shape exploration of discrete geometric structures in the context of freeform architectural design. Our method is formulated as a mesh optimization subject to shape constraints. Our formulation can enforce soft constraints and hard constraints at the same time, and handles equality constraints and inequality constraints in a unified way. We propose a novel numerical solver that splits the optimization into a sequence of simple subproblems that can be solved efficiently and accurately. Based on this algorithm, we develop a system that allows the user to explore designs satisfying geometric constraints. Our system offers full control over the exploration process, by providing direct access to the specification of the design space. At the same time, the complexity of the underlying optimization is hidden from the user, who communicates with the system through intuitive interfaces
Unwind: Interactive Fish Straightening
The ScanAllFish project is a large-scale effort to scan all the world's
33,100 known species of fishes. It has already generated thousands of
volumetric CT scans of fish species which are available on open access
platforms such as the Open Science Framework. To achieve a scanning rate
required for a project of this magnitude, many specimens are grouped together
into a single tube and scanned all at once. The resulting data contain many
fish which are often bent and twisted to fit into the scanner. Our system,
Unwind, is a novel interactive visualization and processing tool which
extracts, unbends, and untwists volumetric images of fish with minimal user
interaction. Our approach enables scientists to interactively unwarp these
volumes to remove the undesired torque and bending using a piecewise-linear
skeleton extracted by averaging isosurfaces of a harmonic function connecting
the head and tail of each fish. The result is a volumetric dataset of a
individual, straight fish in a canonical pose defined by the marine biologist
expert user. We have developed Unwind in collaboration with a team of marine
biologists: Our system has been deployed in their labs, and is presently being
used for dataset construction, biomechanical analysis, and the generation of
figures for scientific publication
Limitations of semidefinite programs for separable states and entangled games
Semidefinite programs (SDPs) are a framework for exact or approximate
optimization that have widespread application in quantum information theory. We
introduce a new method for using reductions to construct integrality gaps for
SDPs. These are based on new limitations on the sum-of-squares (SoS) hierarchy
in approximating two particularly important sets in quantum information theory,
where previously no -round integrality gaps were known: the set of
separable (i.e. unentangled) states, or equivalently, the
norm of a matrix, and the set of quantum correlations; i.e. conditional
probability distributions achievable with local measurements on a shared
entangled state. In both cases no-go theorems were previously known based on
computational assumptions such as the Exponential Time Hypothesis (ETH) which
asserts that 3-SAT requires exponential time to solve. Our unconditional
results achieve the same parameters as all of these previous results (for
separable states) or as some of the previous results (for quantum
correlations). In some cases we can make use of the framework of
Lee-Raghavendra-Steurer (LRS) to establish integrality gaps for any SDP, not
only the SoS hierarchy. Our hardness result on separable states also yields a
dimension lower bound of approximate disentanglers, answering a question of
Watrous and Aaronson et al. These results can be viewed as limitations on the
monogamy principle, the PPT test, the ability of Tsirelson-type bounds to
restrict quantum correlations, as well as the SDP hierarchies of
Doherty-Parrilo-Spedalieri, Navascues-Pironio-Acin and Berta-Fawzi-Scholz.Comment: 47 pages. v2. small changes, fixes and clarifications. published
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Human-in-the-Loop SLAM
Building large-scale, globally consistent maps is a challenging problem, made
more difficult in environments with limited access, sparse features, or when
using data collected by novice users. For such scenarios, where
state-of-the-art mapping algorithms produce globally inconsistent maps, we
introduce a systematic approach to incorporating sparse human corrections,
which we term Human-in-the-Loop Simultaneous Localization and Mapping
(HitL-SLAM). Given an initial factor graph for pose graph SLAM, HitL-SLAM
accepts approximate, potentially erroneous, and rank-deficient human input,
infers the intended correction via expectation maximization (EM),
back-propagates the extracted corrections over the pose graph, and finally
jointly optimizes the factor graph including the human inputs as human
correction factor terms, to yield globally consistent large-scale maps. We thus
contribute an EM formulation for inferring potentially rank-deficient human
corrections to mapping, and human correction factor extensions to the factor
graphs for pose graph SLAM that result in a principled approach to joint
optimization of the pose graph while simultaneously accounting for multiple
forms of human correction. We present empirical results showing the
effectiveness of HitL-SLAM at generating globally accurate and consistent maps
even when given poor initial estimates of the map.Comment: AAAI 201
Learning to Transform Time Series with a Few Examples
We describe a semi-supervised regression algorithm that learns to transform one time series into another time series given examples of the transformation. This algorithm is applied to tracking, where a time series of observations from sensors is transformed to a time series describing the pose of a target. Instead of defining and implementing such transformations for each tracking task separately, our algorithm learns a memoryless transformation of time series from a few example input-output mappings. The algorithm searches for a smooth function that fits the training examples and, when applied to the input time series, produces a time series that evolves according to assumed dynamics. The learning procedure is fast and lends itself to a closed-form solution. It is closely related to nonlinear system identification and manifold learning techniques. We demonstrate our algorithm on the tasks of tracking RFID tags from signal strength measurements, recovering the pose of rigid objects, deformable bodies, and articulated bodies from video sequences. For these tasks, this algorithm requires significantly fewer examples compared to fully-supervised regression algorithms or semi-supervised learning algorithms that do not take the dynamics of the output time series into account
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Reverse Engineering Trimmed NURB Surfaces From Laser Scanned Data
A common reverse engineering problem is to convert several hundred thousand points
collected from the surface of an object via a digitizing process, into a coherent geometric
model that is easily transferred to a CAD software such as a solid modeler for either design
improvement or manufacturing and analysis. These data are very dense and make data-set
manipulation difficult and tedious. Many commercial solutions exist but involve time
consuming interaction to go from points to surface meshes such as BSplines or NURBS (Non
Uniform Rational BSplines). Our approach differs from current industry practice in that we
produce a mesh with little or no interaction from the user. The user can produce degree 2 and
higher BSpline surfaces and can choose the degree and number ofsegments as parameters to
the system. The BSpline surface is both compact and curvature continuous. The former
property reduces the large storage overhead, and the later implies a smooth can be created
from noisy data. In addition, the nature ofthe BSpline allows one to easily and smoothly alter
the surface, making re-engineering extremely feasible. The BSpline surface is created using
the principle ofhigher orders least squares with smoothing functions at the edges. Both linear
and cylindrical data sets are handled using an automated parameterization method. Also,
because ofthe BSpline's continuous nature, a multiresolutional-triangulated mesh can quickly
be produced. This last fact means that an STL file is simple to generate. STL files can also be
easily used as input to the system.Mechanical Engineerin
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