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Fast Probabilistic Fusion of 3D Point Clouds via Occupancy Grids for Scene Classification

Kuhn, Andreas and Huang, Hai and Drauschke, Martin and Mayer, H. (2016) Fast Probabilistic Fusion of 3D Point Clouds via Occupancy Grids for Scene Classification. In: XXIII ISPRS Congress, Technical Commission III, III-3, pp. 325-332. XXIII ISPRS Congress 2016, 12.-19. Juli 2016, Prag, Tschechien. DOI: 10.5194/isprs-annals-III-3-325-2016

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High resolution consumer cameras on Unmanned Aerial Vehicles (UAVs) allow for cheap acquisition of highly detailed images, e.g., of urban regions. Via image registration by means of Structure from Motion (SfM) and Multi View Stereo (MVS) the automatic generation of huge amounts of 3D points with a relative accuracy in the centimeter range is possible. Applications such as semantic classification have a need for accurate 3D point clouds, but do not benefit from an extremely high resolution/density. In this paper, we, therefore, propose a fast fusion of high resolution 3D point clouds based on occupancy grids. The result is used for semantic classification. In contrast to state-of-the-art classification methods, we accept a certain percentage of outliers, arguing that they can be considered in the classification process when a per point belief is determined in the fusion process. To this end, we employ an octree-based fusion which allows for the derivation of outlier probabilities. The probabilities give a belief for every 3D point, which is essential for the semantic classification to consider measurement noise. For an example point cloud with half a billion 3D points (cf. Figure 1), we show that our method can reduce runtime as well as improve classification accuracy and offers high scalability for large datasets.

Item URL in elib:https://elib.dlr.de/109248/
Document Type:Conference or Workshop Item (Speech)
Title:Fast Probabilistic Fusion of 3D Point Clouds via Occupancy Grids for Scene Classification
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Kuhn, Andreasbundeswehr university munichUNSPECIFIED
Huang, Haibundeswehr university munichUNSPECIFIED
Drauschke, MartinMartin.Drauschke (at) dlr.deUNSPECIFIED
Mayer, H.universität der bundeswehr münchenUNSPECIFIED
Date:July 2016
Journal or Publication Title:XXIII ISPRS Congress, Technical Commission III
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
DOI :10.5194/isprs-annals-III-3-325-2016
Page Range:pp. 325-332
Halounova, LenaUNSPECIFIED
Series Name:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Keywords:Scene Classification, Point Cloud Fusion, Multi-View Stereo
Event Title:XXIII ISPRS Congress 2016
Event Location:Prag, Tschechien
Event Type:international Conference
Event Dates:12.-19. Juli 2016
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben Multisensorielle Weltmodellierung
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition
Deposited By: Drauschke, Martin
Deposited On:20 Dec 2016 10:53
Last Modified:31 Jul 2019 20:06

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