914 research outputs found
3D modeling of indoor environments by a mobile platform with a laser scanner and panoramic camera
One major challenge of 3DTV is content acquisition. Here, we present a method to acquire a realistic, visually convincing D model of indoor environments based on a mobile platform that is equipped with a laser range scanner and a panoramic camera. The data of the 2D laser scans are used to solve the simultaneous lo- calization and mapping problem and to extract walls. Textures for walls and floor are built from the images of a calibrated panoramic camera. Multiresolution blending is used to hide seams in the gen- erated textures. The scene is further enriched by 3D-geometry cal- culated from a graph cut stereo technique. We present experimental results from a moderately large real environment.
TetSplat: Real-time Rendering and Volume Clipping of Large Unstructured Tetrahedral Meshes
We present a novel approach to interactive visualization and exploration of large unstructured tetrahedral meshes. These massive 3D meshes are used in mission-critical CFD and structural mechanics simulations, and typically sample multiple field values on several millions of unstructured grid points. Our method relies on the pre-processing of the tetrahedral mesh to partition it into non-convex boundaries and internal fragments that are subsequently encoded into compressed multi-resolution data representations. These compact hierarchical data structures are then adaptively rendered and probed in real-time on a commodity PC. Our point-based rendering algorithm, which is inspired by QSplat, employs a simple but highly efficient splatting technique that guarantees interactive frame-rates regardless of the size of the input mesh and the available rendering hardware. It furthermore allows for real-time probing of the volumetric data-set through constructive solid geometry operations as well as interactive editing of color transfer functions for an arbitrary number of field values. Thus, the presented visualization technique allows end-users for the first time to interactively render and explore very large unstructured tetrahedral meshes on relatively inexpensive hardware
A Multiscale Pyramid Transform for Graph Signals
Multiscale transforms designed to process analog and discrete-time signals
and images cannot be directly applied to analyze high-dimensional data residing
on the vertices of a weighted graph, as they do not capture the intrinsic
geometric structure of the underlying graph data domain. In this paper, we
adapt the Laplacian pyramid transform for signals on Euclidean domains so that
it can be used to analyze high-dimensional data residing on the vertices of a
weighted graph. Our approach is to study existing methods and develop new
methods for the four fundamental operations of graph downsampling, graph
reduction, and filtering and interpolation of signals on graphs. Equipped with
appropriate notions of these operations, we leverage the basic multiscale
constructs and intuitions from classical signal processing to generate a
transform that yields both a multiresolution of graphs and an associated
multiresolution of a graph signal on the underlying sequence of graphs.Comment: 16 pages, 13 figure
GraphX: Unifying Data-Parallel and Graph-Parallel Analytics
From social networks to language modeling, the growing scale and importance
of graph data has driven the development of numerous new graph-parallel systems
(e.g., Pregel, GraphLab). By restricting the computation that can be expressed
and introducing new techniques to partition and distribute the graph, these
systems can efficiently execute iterative graph algorithms orders of magnitude
faster than more general data-parallel systems. However, the same restrictions
that enable the performance gains also make it difficult to express many of the
important stages in a typical graph-analytics pipeline: constructing the graph,
modifying its structure, or expressing computation that spans multiple graphs.
As a consequence, existing graph analytics pipelines compose graph-parallel and
data-parallel systems using external storage systems, leading to extensive data
movement and complicated programming model.
To address these challenges we introduce GraphX, a distributed graph
computation framework that unifies graph-parallel and data-parallel
computation. GraphX provides a small, core set of graph-parallel operators
expressive enough to implement the Pregel and PowerGraph abstractions, yet
simple enough to be cast in relational algebra. GraphX uses a collection of
query optimization techniques such as automatic join rewrites to efficiently
implement these graph-parallel operators. We evaluate GraphX on real-world
graphs and workloads and demonstrate that GraphX achieves comparable
performance as specialized graph computation systems, while outperforming them
in end-to-end graph pipelines. Moreover, GraphX achieves a balance between
expressiveness, performance, and ease of use
3D modeling of indoor environments for a robotic security guard
Autonomous mobile robots will play a major role in future security and surveillance tasks for large scale environments
such as shopping malls, airports, hospitals and museums. Robotic security guards will autonomously survey such environments, unless a remote human operator takes over control. In this context a 3D model can convey much more useful information than the typical 2D maps used in many robotic applications today, both for visualisation of information and as human machine interface for remote control.
This paper addresses the challenge of building such a model of a large environment (50m x 60m) using data from the robot’s own sensors: a 2D laser scanner and a panoramic camera. The data are processed in a pipeline that comprises automatic, semi-automatic and manual stages. The user can interact with the reconstruction process where necessary to ensure robustness and completeness of the model. A hybrid representation, tailored to the application, has been chosen: floors and walls are represented efficiently by textured planes. Non-planar structures like stairs
and tables, which are represented by point clouds, can be added if desired. Our methods to extract these structures include: simultaneous localization and mapping in 2D and wall extraction based on laser scanner range data, building textures from multiple omni-directional images using multi-resolution blending, and calculation of 3D geometry by a graph cut stereo technique. Various renderings illustrate the usability of the model for visualising the security guard’s position and environment
Gaussian Process Regression Adaptive Density-Guided Approach: Towards Calculations of Potential Energy Surfaces for Larger Molecules
We present a new program implementation of the gaussian process regression
adaptive density-guided approach [J. Chem. Phys. 153 (2020) 064105] in the
MidasCpp program. A number of technical and methodological improvements made
allowed us to extend this approach towards calculations of larger molecular
systems than those accessible previously and maintain the very high accuracy of
constructed potential energy surfaces. We demonstrate the performance of this
method on a test set of molecules of growing size and show that up to 80 % of
single point calculations could be avoided introducing a root mean square
deviation in fundamental excitations of about 3 cm. A much higher
accuracy with errors below 1 cm could be achieved with tighter
convergence thresholds still reducing the number of single point computations
by up to 68 %. We further support our findings with a detailed analysis of wall
times measured while employing different electronic structure methods. Our
results demonstrate that GPR-ADGA is an effective tool, which could be applied
for cost-efficient calculations of potential energy surfaces suitable for
highly-accurate vibrational spectra simulations
GIFTed Demons: deformable image registration with local structure-preserving regularization using supervoxels for liver applications.
Deformable image registration, a key component of motion correction in medical imaging, needs to be efficient and provides plausible spatial transformations that reliably approximate biological aspects of complex human organ motion. Standard approaches, such as Demons registration, mostly use Gaussian regularization for organ motion, which, though computationally efficient, rule out their application to intrinsically more complex organ motions, such as sliding interfaces. We propose regularization of motion based on supervoxels, which provides an integrated discontinuity preserving prior for motions, such as sliding. More precisely, we replace Gaussian smoothing by fast, structure-preserving, guided filtering to provide efficient, locally adaptive regularization of the estimated displacement field. We illustrate the approach by applying it to estimate sliding motions at lung and liver interfaces on challenging four-dimensional computed tomography (CT) and dynamic contrast-enhanced magnetic resonance imaging datasets. The results show that guided filter-based regularization improves the accuracy of lung and liver motion correction as compared to Gaussian smoothing. Furthermore, our framework achieves state-of-the-art results on a publicly available CT liver dataset
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