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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2013-10-01
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| 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 |
| Summary: | 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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| ISSN: | 2194-9042 2194-9050 |