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Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Hinterstoisser, S.; Lepetit, V.; Ilic, S.; Holzer, S.; Bradski, G.; Konolige, K.; Navab, N.
Titel:
Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes
Abstract:
We propose a framework for automatic modeling, detection, and tracking of 3D objects with a Kinect. The detection part is mainly based on the recent template-based LINEMOD approach [1] for object detection. We show how to build the templates automatically from 3D models, and how to estimate the 6 degrees-of-freedom pose accurately and in real-time. The pose estimation and the color information allow us to check the detection hypotheses and improves the correct detec- tion rate by 13% with...     »
Stichworte:
CAMP,CAMPComputerVision,ComputerVision,Rigid3DObjectDetection,ACCV
Jahr:
2012
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