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Sparse Volumes for Large Scale 3D Modelling

Funk, Eugen (2015) Sparse Volumes for Large Scale 3D Modelling. Fachdialog Sicherheitsforschung, Berlin.

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Modern emergence of automation in the industry and everyday life is leveraged by extensive research in mobile robotics. Novel 3D sensors such as laser scanners or cameras enable cars to drive autonomously, UAVs to observe critical environments, or an underwater robot to construct pipelines. However, 3D sensor samples do not provide the intrinsic information a robot needs to operate on. Voxel based shape modelling has been identified as a fruitful solution. However, its application is limited to small areas since processing and visualization of large environments is very challenging. Dense voxel grids allow fast data access but suffer from a large memory overhead. Modelling an area of 100x100x100m with a resolution of 1cm would result in a 3.7TB memory requirement. Motivated by this, sparse voxel octrees (SVO) [4] have been proposed. These however, increase the data access complexity from

Item URL in elib:https://elib.dlr.de/96355/
Document Type:Conference or Workshop Item (Poster)
Title:Sparse Volumes for Large Scale 3D Modelling
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Date:April 2015
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Keywords:Big Data, 3D Modelling
Event Title:Fachdialog Sicherheitsforschung
Event Location:Berlin
Event Type:national Conference, Workshop
Organizer:Fraunhofer-Institut für System- und Innovationsforschung ISI
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Terrestrial Vehicles (old)
DLR - Research area:Transport
DLR - Program:V BF - Bodengebundene Fahrzeuge
DLR - Research theme (Project):V - Fahrzeugintelligenz (old)
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Optical Sensor Systems > Information Processing for Optical Systems
Deposited By: Funk, Eugen
Deposited On:01 Jun 2015 06:53
Last Modified:31 Jul 2019 19:53

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