3,309 research outputs found
Acceleration of stereo-matching on multi-core CPU and GPU
This paper presents an accelerated version of a
dense stereo-correspondence algorithm for two different parallelism
enabled architectures, multi-core CPU and GPU. The
algorithm is part of the vision system developed for a binocular
robot-head in the context of the CloPeMa 1 research project.
This research project focuses on the conception of a new clothes
folding robot with real-time and high resolution requirements
for the vision system. The performance analysis shows that
the parallelised stereo-matching algorithm has been significantly
accelerated, maintaining 12x and 176x speed-up respectively
for multi-core CPU and GPU, compared with non-SIMD singlethread
CPU. To analyse the origin of the speed-up and gain
deeper understanding about the choice of the optimal hardware,
the algorithm was broken into key sub-tasks and the performance
was tested for four different hardware architectures
Accelerating Eulerian Fluid Simulation With Convolutional Networks
Efficient simulation of the Navier-Stokes equations for fluid flow is a long
standing problem in applied mathematics, for which state-of-the-art methods
require large compute resources. In this work, we propose a data-driven
approach that leverages the approximation power of deep-learning with the
precision of standard solvers to obtain fast and highly realistic simulations.
Our method solves the incompressible Euler equations using the standard
operator splitting method, in which a large sparse linear system with many free
parameters must be solved. We use a Convolutional Network with a highly
tailored architecture, trained using a novel unsupervised learning framework to
solve the linear system. We present real-time 2D and 3D simulations that
outperform recently proposed data-driven methods; the obtained results are
realistic and show good generalization properties.Comment: Significant revisio
Fast reconstruction of 3D volumes from 2D CT projection data with GPUs
cited By 0International audienceMeso-F.E. modelling of 3D textile composites is a powerful tool, which can help determine mechanical properties and permeability of the reinforcements or composites. The quality of the meso F.E. analyses depends on the quality of the initial model. A direct method based on X-ray tomography imaging is introduced to determine finite element models based on the real geometry of 3D composite reinforcements. The method is particularly suitable regarding 3D textile reinforcements for which internal geometries are numerous and complex. An analysis of the image's texture is performed. A hyperelastic model developed for fibre bundles is used for the simulation of the deformation of the 3D reinforcement. © EDP Sciences, 2016
- …