Object Detection in Multi-view 3D Reconstruction Using Semantic and Geometric Context

We present a method for object detection in a multi view 3D model. We use highly overlapping views, geometric data, and semantic surface classification in order to boost existing 2D algorithms. Specifically, a 3D model is computed from the overlapping views, and the model is segmented into semantic...

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Main Authors: D. Weinshall, A. Golbert
Format: Article
Language:English
Published: Copernicus Publications 2013-10-01
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-W3/97/2013/isprsannals-II-3-W3-97-2013.pdf
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author D. Weinshall
A. Golbert
author_facet D. Weinshall
A. Golbert
author_sort D. Weinshall
collection DOAJ
description We present a method for object detection in a multi view 3D model. We use highly overlapping views, geometric data, and semantic surface classification in order to boost existing 2D algorithms. Specifically, a 3D model is computed from the overlapping views, and the model is segmented into semantic labels using height information, color and planar qualities. 2D detector is run on all images and then detections are mapped into 3D via the model. The detections are clustered in 3D and represented by 3D boxes. Finally, the detections, visibility maps and semantic labels are combined using a Support Vector Machine to achieve a more robust object detector.
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spelling doaj.art-42a415ef4c1044fc94e9eb9871dfc3e22022-12-22T03:49:37ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502013-10-01II-3/W39710210.5194/isprsannals-II-3-W3-97-2013Object Detection in Multi-view 3D Reconstruction Using Semantic and Geometric ContextD. Weinshall0A. Golbert1School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel 91904School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel 91904We present a method for object detection in a multi view 3D model. We use highly overlapping views, geometric data, and semantic surface classification in order to boost existing 2D algorithms. Specifically, a 3D model is computed from the overlapping views, and the model is segmented into semantic labels using height information, color and planar qualities. 2D detector is run on all images and then detections are mapped into 3D via the model. The detections are clustered in 3D and represented by 3D boxes. Finally, the detections, visibility maps and semantic labels are combined using a Support Vector Machine to achieve a more robust object detector.http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-3-W3/97/2013/isprsannals-II-3-W3-97-2013.pdf
spellingShingle D. Weinshall
A. Golbert
Object Detection in Multi-view 3D Reconstruction Using Semantic and Geometric Context
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
title Object Detection in Multi-view 3D Reconstruction Using Semantic and Geometric Context
title_full Object Detection in Multi-view 3D Reconstruction Using Semantic and Geometric Context
title_fullStr Object Detection in Multi-view 3D Reconstruction Using Semantic and Geometric Context
title_full_unstemmed Object Detection in Multi-view 3D Reconstruction Using Semantic and Geometric Context
title_short Object Detection in Multi-view 3D Reconstruction Using Semantic and Geometric Context
title_sort object detection in multi view 3d reconstruction using semantic and geometric context
url http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-3-W3/97/2013/isprsannals-II-3-W3-97-2013.pdf
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