Automatic video to point cloud registration in a structure-from- motion framework

Vidal, Esteban and Piotto, Nicola and Cordara, Giovanni and Morán Burgos, Francisco (2015). Automatic video to point cloud registration in a structure-from- motion framework. In: "International Conference on Image Processing (ICIP 2015)", 27/09/2015 - 30/09/2015, Quebec City, Canada. pp. 2646-2650.

Description

Title: Automatic video to point cloud registration in a structure-from- motion framework
Author/s:
  • Vidal, Esteban
  • Piotto, Nicola
  • Cordara, Giovanni
  • Morán Burgos, Francisco
Item Type: Presentation at Congress or Conference (Article)
Event Title: International Conference on Image Processing (ICIP 2015)
Event Dates: 27/09/2015 - 30/09/2015
Event Location: Quebec City, Canada
Title of Book: International Conference on Image Processing (ICIP 2015)
Título de Revista/Publicación: 2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
Date: 2015
ISSN: 1522-4880
Subjects:
Freetext Keywords: 3D Reconstruction, SfM, Video Registration, Point Cloud Alignment
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

In Structure-from-Motion (SfM) applications, the capability of integrating new visual information into existing 3D models is an important need. In particular, video streams could bring significant advantages, since they provide dense and redundant information, even if normally only relative to a limited portion of the scene. In this work we propose a fast technique to reliably integrate local but dense information from videos into existing global but sparse 3D models. We show how to extract from the video data local 3D information that can be easily processed allowing incremental growing, refinement, and update of the existing 3D models. The proposed technique has been tested against two state-of-the-art SfM algorithms, showing significant improvements in terms of computational time and final point cloud density.

Funding Projects

TypeCodeAcronymLeaderTitle
FP7610691BRIDGETThe University of SurreyBRIDging the Gap for Enhanced broadcasT

More information

Item ID: 41384
DC Identifier: http://oa.upm.es/41384/
OAI Identifier: oai:oa.upm.es:41384
Official URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7351282
Deposited by: Memoria Investigacion
Deposited on: 10 Jul 2016 07:29
Last Modified: 10 Jul 2016 07:29
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