Evaluation of sampling method effects in 3D non-rigid registration

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Title: Evaluation of sampling method effects in 3D non-rigid registration
Authors: Saval-Calvo, Marcelo | Azorin-Lopez, Jorge | Fuster-Guilló, Andrés | Garcia-Rodriguez, Jose | Orts-Escolano, Sergio | Garcia-Garcia, Alberto
Research Group/s: Informática Industrial y Redes de Computadores
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación
Keywords: 3D downsampling | Non-rigid registration | Color registration
Knowledge Area: Arquitectura y Tecnología de Computadores
Issue Date: 15-Mar-2016
Publisher: Springer London
Citation: Neural Computing and Applications. 2017, 28(5): 953-967. doi:10.1007/s00521-016-2258-z
Abstract: Since the beginning of 3D computer vision problems, the use of techniques to reduce the data to make it treatable preserving the important aspects of the scene has been necessary. Currently, with the new low-cost RGB-D sensors, which provide a stream of color and 3D data of approximately 30 frames per second, this is getting more relevance. Many applications make use of these sensors and need a preprocessing to downsample the data in order to either reduce the processing time or improve the data (e.g., reducing noise or enhancing the important features). In this paper, we present a comparison of different downsampling techniques which are based on different principles. Concretely, five different downsampling methods are included: a bilinear-based method, a normal-based, a color-based, a combination of the normal and color-based samplings, and a growing neural gas (GNG)-based approach. For the comparison, two different models have been used acquired with the Blensor software. Moreover, to evaluate the effect of the downsampling in a real application, a 3D non-rigid registration is performed with the data sampled. From the experimentation we can conclude that depending on the purpose of the application some kernels of the sampling methods can improve drastically the results. Bilinear- and GNG-based methods provide homogeneous point clouds, but color-based and normal-based provide datasets with higher density of points in areas with specific features. In the non-rigid application, if a color-based sampled point cloud is used, it is possible to properly register two datasets for cases where intensity data are relevant in the model and outperform the results if only a homogeneous sampling is used.
Sponsor: This study was supported in part by the University of Alicante and Spanish government under Grants GRE11-01 and DPI2013-40534-R.
URI: http://hdl.handle.net/10045/55365
ISSN: 0941-0643 (Print) | 1433-3058 (Online)
DOI: 10.1007/s00521-016-2258-z
Language: eng
Type: info:eu-repo/semantics/article
Rights: © The Natural Computing Applications Forum 2016. The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-016-2258-z
Peer Review: si
Publisher version: http://dx.doi.org/10.1007/s00521-016-2258-z
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