3D SUPER-RESOLUTION APPROACH FOR SPARSE LASER SCANNER DATA

Laser scanner point cloud has been emerging in Photogrammetry and computer vision to achieve high level tasks such as object tracking, object recognition and scene understanding. However, low cost laser scanners are noisy, sparse and prone to systematic errors. This paper proposes a novel 3D super r...

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Main Authors: S. Hosseinyalamdary, A. Yilmaz
Format: Article
Language:English
Published: Copernicus Publications 2015-08-01
Series:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-3-W5/151/2015/isprsannals-II-3-W5-151-2015.pdf
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author S. Hosseinyalamdary
A. Yilmaz
author_facet S. Hosseinyalamdary
A. Yilmaz
author_sort S. Hosseinyalamdary
collection DOAJ
description Laser scanner point cloud has been emerging in Photogrammetry and computer vision to achieve high level tasks such as object tracking, object recognition and scene understanding. However, low cost laser scanners are noisy, sparse and prone to systematic errors. This paper proposes a novel 3D super resolution approach to reconstruct surface of the objects in the scene. This method works on sparse, unorganized point clouds and has superior performance over other surface recovery approaches. Since the proposed approach uses anisotropic diffusion equation, it does not deteriorate the object boundaries and it preserves topology of the object.
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spelling doaj.art-7d06bb21256447388cf684e4fff3f0892022-12-21T19:34:20ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502015-08-01II-3-W515115710.5194/isprsannals-II-3-W5-151-20153D SUPER-RESOLUTION APPROACH FOR SPARSE LASER SCANNER DATAS. Hosseinyalamdary0A. Yilmaz1Photogrammetric Computer Vision (PCV) Lab 2070 Neil Avenue, Columbus, OH 43212, USAPhotogrammetric Computer Vision (PCV) Lab 2070 Neil Avenue, Columbus, OH 43212, USALaser scanner point cloud has been emerging in Photogrammetry and computer vision to achieve high level tasks such as object tracking, object recognition and scene understanding. However, low cost laser scanners are noisy, sparse and prone to systematic errors. This paper proposes a novel 3D super resolution approach to reconstruct surface of the objects in the scene. This method works on sparse, unorganized point clouds and has superior performance over other surface recovery approaches. Since the proposed approach uses anisotropic diffusion equation, it does not deteriorate the object boundaries and it preserves topology of the object.http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-3-W5/151/2015/isprsannals-II-3-W5-151-2015.pdf
spellingShingle S. Hosseinyalamdary
A. Yilmaz
3D SUPER-RESOLUTION APPROACH FOR SPARSE LASER SCANNER DATA
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
title 3D SUPER-RESOLUTION APPROACH FOR SPARSE LASER SCANNER DATA
title_full 3D SUPER-RESOLUTION APPROACH FOR SPARSE LASER SCANNER DATA
title_fullStr 3D SUPER-RESOLUTION APPROACH FOR SPARSE LASER SCANNER DATA
title_full_unstemmed 3D SUPER-RESOLUTION APPROACH FOR SPARSE LASER SCANNER DATA
title_short 3D SUPER-RESOLUTION APPROACH FOR SPARSE LASER SCANNER DATA
title_sort 3d super resolution approach for sparse laser scanner data
url http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-3-W5/151/2015/isprsannals-II-3-W5-151-2015.pdf
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