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• W1521366206 abstract "We propose a method for recognizing rigid object instances in RGB-D point clouds by accumulating low-level information from keypoint correspondences over multiple observations. Compared to existing multi-view approaches, we make fewer assumptions on the recognition problem, dealing with cluttered and partially dynamic environments as well as covering a wide range of objects. Evaluation on the publicly available TUW and Willow datasets showed that our method achieves state-of-the-art recognition performance for challenging sequences of static environments and a significant improvement for environments partially changing during the observation." @default.
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• W1521366206 date "2015-05-01" @default.
• W1521366206 modified "2023-10-18" @default.
• W1521366206 title "Temporal integration of feature correspondences for enhanced recognition in cluttered and dynamic environments" @default.
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• W1521366206 doi "https://doi.org/10.1109/icra.2015.7139611" @default.
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