3D model reconstruction using neural gas accelerated on GPU

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Title: 3D model reconstruction using neural gas accelerated on GPU
Authors: Orts-Escolano, Sergio | Garcia-Rodriguez, Jose | Serra Pérez, José Antonio | Jimeno-Morenilla, Antonio | Garcia-Garcia, Alberto | Morell, Vicente | Cazorla, Miguel
Research Group/s: Informática Industrial y Redes de Computadores | UniCAD: Grupo de Investigación en CAD/CAM/CAE de la Universidad de Alicante | Robótica y Visión Tridimensional (RoViT)
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: Neural gas | Topology preservation | 3D model reconstruction | Graphics Processing Units
Knowledge Area: Arquitectura y Tecnología de Computadores | Ciencia de la Computación e Inteligencia Artificial
Issue Date: Jul-2015
Publisher: Elsevier
Citation: Applied Soft Computing. 2015, 32: 87-100. doi:10.1016/j.asoc.2015.03.042
Abstract: In this work, we propose the use of the neural gas (NG), a neural network that uses an unsupervised Competitive Hebbian Learning (CHL) rule, to develop a reverse engineering process. This is a simple and accurate method to reconstruct objects from point clouds obtained from multiple overlapping views using low-cost sensors. In contrast to other methods that may need several stages that include downsampling, noise filtering and many other tasks, the NG automatically obtains the 3D model of the scanned objects. To demonstrate the validity of our proposal we tested our method with several models and performed a study of the neural network parameterization computing the quality of representation and also comparing results with other neural methods like growing neural gas and Kohonen maps or classical methods like Voxel Grid. We also reconstructed models acquired by low cost sensors that can be used in virtual and augmented reality environments for redesign or manipulation purposes. Since the NG algorithm has a strong computational cost we propose its acceleration. We have redesigned and implemented the NG learning algorithm to fit it onto Graphics Processing Units using CUDA. A speed-up of 180× faster is obtained compared to the sequential CPU version.
Sponsor: This work was partially funded by the Spanish Government DPI2013-40534-R grant.
URI: http://hdl.handle.net/10045/53499
ISSN: 1568-4946 (Print) | 1872-9681 (Online)
DOI: 10.1016/j.asoc.2015.03.042
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2015 Elsevier B.V.
Peer Review: si
Publisher version: http://dx.doi.org/10.1016/j.asoc.2015.03.042
Appears in Collections:INV - I2RC - Artículos de Revistas
INV - UNICAD - Artículos de Revistas
INV - RoViT - Artículos de Revistas

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