Kuhn, Andreas und Huang, Hai und Drauschke, Martin und 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, Seiten 325-332. XXIII ISPRS Congress 2016, 2016-07-12 - 2016-07-19, Prag, Tschechien. doi: 10.5194/isprs-annals-III-3-325-2016.
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Kurzfassung
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.
elib-URL des Eintrags: | https://elib.dlr.de/109248/ | ||||||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||||||
Titel: | Fast Probabilistic Fusion of 3D Point Clouds via Occupancy Grids for Scene Classification | ||||||||||||||||||||
Autoren: |
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Datum: | Juli 2016 | ||||||||||||||||||||
Erschienen in: | XXIII ISPRS Congress, Technical Commission III | ||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||
In SCOPUS: | Nein | ||||||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||||||
Band: | III-3 | ||||||||||||||||||||
DOI: | 10.5194/isprs-annals-III-3-325-2016 | ||||||||||||||||||||
Seitenbereich: | Seiten 325-332 | ||||||||||||||||||||
Herausgeber: |
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Name der Reihe: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences | ||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||
Stichwörter: | Scene Classification, Point Cloud Fusion, Multi-View Stereo | ||||||||||||||||||||
Veranstaltungstitel: | XXIII ISPRS Congress 2016 | ||||||||||||||||||||
Veranstaltungsort: | Prag, Tschechien | ||||||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||
Veranstaltungsbeginn: | 12 Juli 2016 | ||||||||||||||||||||
Veranstaltungsende: | 19 Juli 2016 | ||||||||||||||||||||
Veranstalter : | ISPRS | ||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||
HGF - Programmthema: | Technik für Raumfahrtsysteme | ||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||
DLR - Forschungsgebiet: | R SY - Technik für Raumfahrtsysteme | ||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Vorhaben Multisensorielle Weltmodellierung (alt) | ||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||
Institute & Einrichtungen: | Institut für Robotik und Mechatronik (ab 2013) > Perzeption und Kognition | ||||||||||||||||||||
Hinterlegt von: | Drauschke, Martin | ||||||||||||||||||||
Hinterlegt am: | 20 Dez 2016 10:53 | ||||||||||||||||||||
Letzte Änderung: | 24 Apr 2024 20:14 |
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