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      Accurate Object Pose Estimation Using Depth Only

      research-article
      * ,
      Sensors (Basel, Switzerland)
      MDPI
      pose estimation, point pair feature, point cloud

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          Abstract

          Object recognition and pose estimation is an important task in computer vision. A pose estimation algorithm using only depth information is proposed in this paper. Foreground and background points are distinguished based on their relative positions with boundaries. Model templates are selected using synthetic scenes to make up for the point pair feature algorithm. An accurate and fast pose verification method is introduced to select result poses from thousands of poses. Our algorithm is evaluated against a large number of scenes and proved to be more accurate than algorithms using both color information and depth information.

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          Most cited references27

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          Watersheds in digital spaces: an efficient algorithm based on immersion simulations

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            Iterative point matching for registration of free-form curves and surfaces

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              SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                30 March 2018
                April 2018
                : 18
                : 4
                : 1045
                Affiliations
                Graduate School of Information Sciences, Tohoku University, Aramaki Aza Aoba 6-6-01, Aoba-Ku, Sendai 980-8579, Japan; koichi@ 123456m.tohoku.ac.jp
                Author notes
                [* ]Correspondence: li.mingyu.s8@ 123456dc.tohoku.ac.jp ; Tel.: +81-090-6688-4828
                Author information
                https://orcid.org/0000-0001-7422-7759
                https://orcid.org/0000-0002-4473-2698
                Article
                sensors-18-01045
                10.3390/s18041045
                5948643
                29601549
                a14d7e76-c9dd-484b-bf57-e60f078694bb
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 14 February 2018
                : 28 March 2018
                Categories
                Article

                Biomedical engineering
                pose estimation,point pair feature,point cloud
                Biomedical engineering
                pose estimation, point pair feature, point cloud

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