18,110 research outputs found
Visibility Representations of Boxes in 2.5 Dimensions
We initiate the study of 2.5D box visibility representations (2.5D-BR) where
vertices are mapped to 3D boxes having the bottom face in the plane and
edges are unobstructed lines of sight parallel to the - or -axis. We
prove that: Every complete bipartite graph admits a 2.5D-BR; The
complete graph admits a 2.5D-BR if and only if ; Every
graph with pathwidth at most admits a 2.5D-BR, which can be computed in
linear time. We then turn our attention to 2.5D grid box representations
(2.5D-GBR) which are 2.5D-BRs such that the bottom face of every box is a unit
square at integer coordinates. We show that an -vertex graph that admits a
2.5D-GBR has at most edges and this bound is tight. Finally,
we prove that deciding whether a given graph admits a 2.5D-GBR with a given
footprint is NP-complete. The footprint of a 2.5D-BR is the set of
bottom faces of the boxes in .Comment: Appears in the Proceedings of the 24th International Symposium on
Graph Drawing and Network Visualization (GD 2016
3D Visibility Representations of 1-planar Graphs
We prove that every 1-planar graph G has a z-parallel visibility
representation, i.e., a 3D visibility representation in which the vertices are
isothetic disjoint rectangles parallel to the xy-plane, and the edges are
unobstructed z-parallel visibilities between pairs of rectangles. In addition,
the constructed representation is such that there is a plane that intersects
all the rectangles, and this intersection defines a bar 1-visibility
representation of G.Comment: Appears in the Proceedings of the 25th International Symposium on
Graph Drawing and Network Visualization (GD 2017
Horizontal visibility graphs transformed from fractional Brownian motions: Topological properties versus Hurst index
Nonlinear time series analysis aims at understanding the dynamics of
stochastic or chaotic processes. In recent years, quite a few methods have been
proposed to transform a single time series to a complex network so that the
dynamics of the process can be understood by investigating the topological
properties of the network. We study the topological properties of horizontal
visibility graphs constructed from fractional Brownian motions with different
Hurst index . Special attention has been paid to the impact of Hurst
index on the topological properties. It is found that the clustering
coefficient decreases when increases. We also found that the mean
length of the shortest paths increases exponentially with for fixed
length of the original time series. In addition, increases linearly
with respect to when is close to 1 and in a logarithmic form when
is close to 0. Although the occurrence of different motifs changes with ,
the motif rank pattern remains unchanged for different . Adopting the
node-covering box-counting method, the horizontal visibility graphs are found
to be fractals and the fractal dimension decreases with . Furthermore,
the Pearson coefficients of the networks are positive and the degree-degree
correlations increase with the degree, which indicate that the horizontal
visibility graphs are assortative. With the increase of , the Pearson
coefficient decreases first and then increases, in which the turning point is
around . The presence of both fractality and assortativity in the
horizontal visibility graphs converted from fractional Brownian motions is
different from many cases where fractal networks are usually disassortative.Comment: 12 pages, 8 figure
PIXOR: Real-time 3D Object Detection from Point Clouds
We address the problem of real-time 3D object detection from point clouds in
the context of autonomous driving. Computation speed is critical as detection
is a necessary component for safety. Existing approaches are, however,
expensive in computation due to high dimensionality of point clouds. We utilize
the 3D data more efficiently by representing the scene from the Bird's Eye View
(BEV), and propose PIXOR, a proposal-free, single-stage detector that outputs
oriented 3D object estimates decoded from pixel-wise neural network
predictions. The input representation, network architecture, and model
optimization are especially designed to balance high accuracy and real-time
efficiency. We validate PIXOR on two datasets: the KITTI BEV object detection
benchmark, and a large-scale 3D vehicle detection benchmark. In both datasets
we show that the proposed detector surpasses other state-of-the-art methods
notably in terms of Average Precision (AP), while still runs at >28 FPS.Comment: Update of CVPR2018 paper: correct timing, fix typos, add
acknowledgemen
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
We propose a real-time RGB-based pipeline for object detection and 6D pose
estimation. Our novel 3D orientation estimation is based on a variant of the
Denoising Autoencoder that is trained on simulated views of a 3D model using
Domain Randomization. This so-called Augmented Autoencoder has several
advantages over existing methods: It does not require real, pose-annotated
training data, generalizes to various test sensors and inherently handles
object and view symmetries. Instead of learning an explicit mapping from input
images to object poses, it provides an implicit representation of object
orientations defined by samples in a latent space. Our pipeline achieves
state-of-the-art performance on the T-LESS dataset both in the RGB and RGB-D
domain. We also evaluate on the LineMOD dataset where we can compete with other
synthetically trained approaches. We further increase performance by correcting
3D orientation estimates to account for perspective errors when the object
deviates from the image center and show extended results.Comment: Code available at: https://github.com/DLR-RM/AugmentedAutoencode
BeSpaceD: Towards a Tool Framework and Methodology for the Specification and Verification of Spatial Behavior of Distributed Software Component Systems
In this report, we present work towards a framework for modeling and checking
behavior of spatially distributed component systems. Design goals of our
framework are the ability to model spatial behavior in a component oriented,
simple and intuitive way, the possibility to automatically analyse and verify
systems and integration possibilities with other modeling and verification
tools. We present examples and the verification steps necessary to prove
properties such as range coverage or the absence of collisions between
components and technical details
Colored anchored visibility representations in 2D and 3D space
© 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In a visibility representation of a graph G, the vertices are represented by nonoverlapping geometric objects, while the edges are represented as segments that only intersect the geometric objects associated with their end-vertices. Given a set P of n points, an Anchored Visibility Representation of a graph G with n vertices is a visibility representation such that for each vertex v of G, the geometric object representing v contains a point of P. We prove positive and negative results about the existence of anchored visibility representations under various models, both in 2D and in 3D space. We consider the case when the mapping between the vertices and the points is not given and the case when it is only partially given.Peer ReviewedPostprint (author's final draft
Efficient Cluster Algorithm for CP(N-1) Models
Despite several attempts, no efficient cluster algorithm has been constructed
for CP(N-1) models in the standard Wilson formulation of lattice field theory.
In fact, there is a no-go theorem that prevents the construction of an
efficient Wolff-type embedding algorithm. In this paper, we construct an
efficient cluster algorithm for ferromagnetic SU(N)-symmetric quantum spin
systems. Such systems provide a regularization for CP(N-1) models in the
framework of D-theory. We present detailed studies of the autocorrelations and
find a dynamical critical exponent that is consistent with z = 0.Comment: 14 pages, 3 figure
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