Real-time 3D semi-local surface patch extraction using GPGPU

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/35758
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Title: Real-time 3D semi-local surface patch extraction using GPGPU
Authors: Orts-Escolano, Sergio | Morell, Vicente | Garcia-Rodriguez, Jose | Cazorla, Miguel | Fisher, Robert B.
Research Group/s: Robótica y Visión Tridimensional (RoViT) | Informática Industrial y Redes de Computadores
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: Real-time | GPGPU | RGBD-data | 3D local shape descriptor | Object recognition
Knowledge Area: Arquitectura y Tecnología de Computadores | Ciencia de la Computación e Inteligencia Artificial
Issue Date: Dec-2013
Publisher: Springer Berlin Heidelberg
Citation: Journal of Real-Time Image Processing. 2013, Dec. doi:10.1007/s11554-013-0385-7
Abstract: Feature vectors can be anything from simple surface normals to more complex feature descriptors. Feature extraction is important to solve various computer vision problems: e.g. registration, object recognition and scene understanding. Most of these techniques cannot be computed online due to their complexity and the context where they are applied. Therefore, computing these features in real-time for many points in the scene is impossible. In this work, a hardware-based implementation of 3D feature extraction and 3D object recognition is proposed to accelerate these methods and therefore the entire pipeline of RGBD based computer vision systems where such features are typically used. The use of a GPU as a general purpose processor can achieve considerable speed-ups compared with a CPU implementation. In this work, advantageous results are obtained using the GPU to accelerate the computation of a 3D descriptor based on the calculation of 3D semi-local surface patches of partial views. This allows descriptor computation at several points of a scene in real-time. Benefits of the accelerated descriptor have been demonstrated in object recognition tasks. Source code will be made publicly available as contribution to the Open Source Point Cloud Library.
Sponsor: This work was partially funded by the Valencian Government BEFPI/2012/056, and by the European Network of Excellence on High Performance and Embedded Architecture and Compilation (HiPEAC).
URI: http://hdl.handle.net/10045/35758
ISSN: 1861-8200 (Print) | 1861-8219 (Online)
DOI: 10.1007/s11554-013-0385-7
Language: eng
Type: info:eu-repo/semantics/article
Rights: The original publication is available at www.springerlink.com
Peer Review: si
Publisher version: http://dx.doi.org/10.1007/s11554-013-0385-7
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INV - I2RC - Artículos de Revistas

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