Compression and registration of 3D point clouds using GMMs

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Title: Compression and registration of 3D point clouds using GMMs
Authors: Navarrete Sánchez, Javier | Viejo Hernando, Diego | Cazorla, Miguel
Research Group/s: Robótica y Visión Tridimensional (RoViT)
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial | Universidad de Alicante. Instituto Universitario de Investigación Informática
Keywords: 3D compression | 3D registration
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Issue Date: 15-Jul-2018
Publisher: Elsevier
Citation: Pattern Recognition Letters. 2018, 110: 8-15. doi:10.1016/j.patrec.2018.03.017
Abstract: 3D data sensors provide an enormous amount of information. It is necessary to develop efficient methods to manage this information under certain time, bandwidth or storage space requirements. In this work, we propose a 3D compression and decompression method. This method also allows the use of the compressed data for a registration process. First, points are selected and grouped, using a 3D-model based on planar surfaces. Next, we use a fast variant of Gaussian Mixture Models and an Expectation-Maximization algorithm to replace the points grouped in the previous step with a set of Gaussian distributions. These learned models can be used as features to find matches between two consecutive poses and apply 3D pose registration using RANSAC. Finally, the 3D map can be obtained by decompressing the models.
Sponsor: This work has been supported by the Spanish Government TIN2016-76515-R Grant, supported with Feder funds.
URI: http://hdl.handle.net/10045/74609
ISSN: 0167-8655 (Print) | 1872-7344 (Online)
DOI: 10.1016/j.patrec.2018.03.017
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2018 Elsevier B.V.
Peer Review: si
Publisher version: https://doi.org/10.1016/j.patrec.2018.03.017
Appears in Collections:INV - RoViT - Artículos de Revistas

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