311 research outputs found
Real-Time Dense Stereo Matching With ELAS on FPGA Accelerated Embedded Devices
For many applications in low-power real-time robotics, stereo cameras are the
sensors of choice for depth perception as they are typically cheaper and more
versatile than their active counterparts. Their biggest drawback, however, is
that they do not directly sense depth maps; instead, these must be estimated
through data-intensive processes. Therefore, appropriate algorithm selection
plays an important role in achieving the desired performance characteristics.
Motivated by applications in space and mobile robotics, we implement and
evaluate a FPGA-accelerated adaptation of the ELAS algorithm. Despite offering
one of the best trade-offs between efficiency and accuracy, ELAS has only been
shown to run at 1.5-3 fps on a high-end CPU. Our system preserves all
intriguing properties of the original algorithm, such as the slanted plane
priors, but can achieve a frame rate of 47fps whilst consuming under 4W of
power. Unlike previous FPGA based designs, we take advantage of both components
on the CPU/FPGA System-on-Chip to showcase the strategy necessary to accelerate
more complex and computationally diverse algorithms for such low power,
real-time systems.Comment: 8 pages, 7 figures, 2 table
Polylidar3D -- Fast Polygon Extraction from 3D Data
Flat surfaces captured by 3D point clouds are often used for localization,
mapping, and modeling. Dense point cloud processing has high computation and
memory costs making low-dimensional representations of flat surfaces such as
polygons desirable. We present Polylidar3D, a non-convex polygon extraction
algorithm which takes as input unorganized 3D point clouds (e.g., LiDAR data),
organized point clouds (e.g., range images), or user-provided meshes.
Non-convex polygons represent flat surfaces in an environment with interior
cutouts representing obstacles or holes. The Polylidar3D front-end transforms
input data into a half-edge triangular mesh. This representation provides a
common level of input data abstraction for subsequent back-end processing. The
Polylidar3D back-end is composed of four core algorithms: mesh smoothing,
dominant plane normal estimation, planar segment extraction, and finally
polygon extraction. Polylidar3D is shown to be quite fast, making use of CPU
multi-threading and GPU acceleration when available. We demonstrate
Polylidar3D's versatility and speed with real-world datasets including aerial
LiDAR point clouds for rooftop mapping, autonomous driving LiDAR point clouds
for road surface detection, and RGBD cameras for indoor floor/wall detection.
We also evaluate Polylidar3D on a challenging planar segmentation benchmark
dataset. Results consistently show excellent speed and accuracy.Comment: 40 page
Haptic Interaction with 3D oriented point clouds on the GPU
Real-time point-based rendering and interaction with virtual objects is gaining popularity
and importance as di�erent haptic devices and technologies increasingly provide the basis
for realistic interaction. Haptic Interaction is being used for a wide range of applications
such as medical training, remote robot operators, tactile displays and video games. Virtual
object visualization and interaction using haptic devices is the main focus; this process
involves several steps such as: Data Acquisition, Graphic Rendering, Haptic Interaction
and Data Modi�cation. This work presents a framework for Haptic Interaction using the
GPU as a hardware accelerator, and includes an approach for enabling the modi�cation
of data during interaction. The results demonstrate the limits and capabilities of these
techniques in the context of volume rendering for haptic applications. Also, the use
of dynamic parallelism as a technique to scale the number of threads needed from the
accelerator according to the interaction requirements is studied allowing the editing of
data sets of up to one million points at interactive haptic frame rates
Primary arm array during directional solidification of a single-crystal binary alloy: Large-scale phase-field study
AbstractPrimary arm arrays formed during the directional solidification of a single-crystal binary alloy were investigated by performing large-scale phase-field simulations using the GPU supercomputer TSUBAME2.5 at Tokyo Institute of Technology. The primary arm array and spacing were investigated by Voronoi decomposition and Delaunay triangulation, respectively. It was concluded that a hexagonal array was dominant for both the dendrite and cell structures and that penta–hepta defects, which are typical defects in hexagonal patterns, were formed. The primary arms continuously moved such that the number of hexagons increased, and the distribution of primary arm spacing became uniform over time even after the number of primary arms was constant. The order of array was highest in the growth condition of the dendrite close to the cell-to-dendrite transition region. In addition, we proposed a realistic and accurate evaluation method of primary arm array by removing small sides from the Voronoi polygons
Geometric algorithms for cavity detection on protein surfaces
Macromolecular structures such as proteins heavily empower cellular processes or functions.
These biological functions result from interactions between proteins and peptides,
catalytic substrates, nucleotides or even human-made chemicals. Thus, several
interactions can be distinguished: protein-ligand, protein-protein, protein-DNA,
and so on. Furthermore, those interactions only happen under chemical- and shapecomplementarity
conditions, and usually take place in regions known as binding sites.
Typically, a protein consists of four structural levels. The primary structure of a protein
is made up of its amino acid sequences (or chains). Its secondary structure essentially
comprises -helices and -sheets, which are sub-sequences (or sub-domains) of amino
acids of the primary structure. Its tertiary structure results from the composition of
sub-domains into domains, which represent the geometric shape of the protein. Finally,
the quaternary structure of a protein results from the aggregate of two or more
tertiary structures, usually known as a protein complex.
This thesis fits in the scope of structure-based drug design and protein docking. Specifically,
one addresses the fundamental problem of detecting and identifying protein
cavities, which are often seen as tentative binding sites for ligands in protein-ligand
interactions. In general, cavity prediction algorithms split into three main categories:
energy-based, geometry-based, and evolution-based. Evolutionary methods build upon
evolutionary sequence conservation estimates; that is, these methods allow us to detect
functional sites through the computation of the evolutionary conservation of the
positions of amino acids in proteins. Energy-based methods build upon the computation
of interaction energies between protein and ligand atoms. In turn, geometry-based algorithms
build upon the analysis of the geometric shape of the protein (i.e., its tertiary
structure) to identify cavities. This thesis focuses on geometric methods.
We introduce here three new geometric-based algorithms for protein cavity detection.
The main contribution of this thesis lies in the use of computer graphics techniques
in the analysis and recognition of cavities in proteins, much in the spirit of molecular
graphics and modeling. As seen further ahead, these techniques include field-of-view
(FoV), voxel ray casting, back-face culling, shape diameter functions, Morse theory,
and critical points. The leading idea is to come up with protein shape segmentation,
much like we commonly do in mesh segmentation in computer graphics. In practice,
protein cavity algorithms are nothing more than segmentation algorithms designed for
proteins.Estruturas macromoleculares tais como as proteínas potencializam processos ou funções
celulares. Estas funções resultam das interações entre proteínas e peptídeos, substratos
catalíticos, nucleótideos, ou até mesmo substâncias químicas produzidas pelo
homem. Assim, há vários tipos de interacções: proteína-ligante, proteína-proteína,
proteína-DNA e assim por diante. Além disso, estas interações geralmente ocorrem em
regiões conhecidas como locais de ligação (binding sites, do inglês) e só acontecem sob
condições de complementaridade química e de forma. É também importante referir que
uma proteína pode ser estruturada em quatro níveis. A estrutura primária que consiste
em sequências de aminoácidos (ou cadeias), a estrutura secundária que compreende
essencialmente por hélices e folhas , que são subsequências (ou subdomínios) dos
aminoácidos da estrutura primária, a estrutura terciária que resulta da composição de
subdomínios em domínios, que por sua vez representa a forma geométrica da proteína,
e por fim a estrutura quaternária que é o resultado da agregação de duas ou mais estruturas
terciárias. Este último nível estrutural é frequentemente conhecido por um
complexo proteico.
Esta tese enquadra-se no âmbito da conceção de fármacos baseados em estrutura e no
acoplamento de proteínas. Mais especificamente, aborda-se o problema fundamental
da deteção e identificação de cavidades que são frequentemente vistos como possíveis
locais de ligação (putative binding sites, do inglês) para os seus ligantes (ligands, do
inglês). De forma geral, os algoritmos de identificação de cavidades dividem-se em três
categorias principais: baseados em energia, geometria ou evolução. Os métodos evolutivos
baseiam-se em estimativas de conservação das sequências evolucionárias. Isto é,
estes métodos permitem detectar locais funcionais através do cálculo da conservação
evolutiva das posições dos aminoácidos das proteínas. Em relação aos métodos baseados
em energia estes baseiam-se no cálculo das energias de interação entre átomos
da proteína e do ligante. Por fim, os algoritmos geométricos baseiam-se na análise da
forma geométrica da proteína para identificar cavidades. Esta tese foca-se nos métodos
geométricos.
Apresentamos nesta tese três novos algoritmos geométricos para detecção de cavidades
em proteínas. A principal contribuição desta tese está no uso de técnicas de computação
gráfica na análise e reconhecimento de cavidades em proteínas, muito no espírito da
modelação e visualização molecular. Como pode ser visto mais à frente, estas técnicas
incluem o field-of-view (FoV), voxel ray casting, back-face culling, funções de diâmetro
de forma, a teoria de Morse, e os pontos críticos. A ideia principal é segmentar a
proteína, à semelhança do que acontece na segmentação de malhas em computação
gráfica. Na prática, os algoritmos de detecção de cavidades não são nada mais que
algoritmos de segmentação de proteínas
Real-time stress analysis of three-dimensional boundary element problems with continuously updating geometry
Computational design of mechanical components is an iterative process that involves multiple stress analysis runs; this can be time consuming and expensive. Significant improvements in the efficiency of this process can be made by increasing the level of interactivity. One approach is through real-time re-analysis of models with continuously updating geometry. In this work the boundary element method is used to realise this vision. Three primary areas need to be considered to accelerate the re-solution of boundary element problems. These are re-meshing the model, updating the boundary element system of equations and re-solution of the system.
Once the initial model has been constructed and solved, the user may apply geometric perturbations to parts of the model. A new re-meshing algorithm accommodates these changes in geometry whilst retaining as much of the existing mesh as possible. This allows the majority of the previous boundary element system of equations to be re-used for the new analysis.
Efficiency is achieved during re-integration by applying a reusable intrinsic sample point (RISP) integration scheme with a 64-bit single precision code. Parts of the boundary element system that have not been updated are retained by the re-analysis and integrals that multiply zero boundary conditions are suppressed. For models with fewer than 10,000 degrees of freedom, the re-integration algorithm performs up to five times faster than a standard integration scheme with less than 0.15% reduction in the L_2-norm accuracy of the solution vector. The method parallelises easily and an additional six times speed-up can be achieved on eight processors over the serial implementation.
The performance of a range of direct, iterative and reduction based linear solvers have been compared for solving the boundary element system with the iterative generalised minimal residual (GMRES) solver providing the fastest convergence rate and the most accurate result. Further time savings are made by preconditioning the updated system with the LU decomposition of the original system. Using these techniques, near real-time analysis can be achieved for three-dimensional simulations; for two-dimensional models such real-time performance has already been demonstrated
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