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

D. Weinshall, A. Golbert

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

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.

Original languageAmerican English
Pages (from-to)97-102
Number of pages6
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume2
Issue number3W3
DOIs
StatePublished - 8 Oct 2013
EventJoint Workshop on Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms and Evaluation, CMRT 2013 - Antalya, Turkey
Duration: 12 Nov 201313 Nov 2013

Keywords

  • 3D Virtual City
  • Context
  • Object Detection
  • Segmentation
  • Semantic Classification

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