@InProceedings{LimaTeic:2016:EfGlPo,
               author = "Lima, Jo{\~a}o Paulo Silva do Monte and Teichrieb, Veronica",
          affiliation = "{Universidade Federal Rural de Pernambuco} and {Universidade 
                         Federal de Pernambuco}",
                title = "An Efficient Global Point Cloud Descriptor for Object Recognition 
                         and Pose Estimation",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "IEEE Computer Society´s Conference Publishing Services",
              address = "Los Alamitos",
             keywords = "cloud descriptor, object recognition, pose estimation.",
             abstract = "This paper presents a global point cloud descriptor to be used for 
                         efficient object recognition and pose estimation. The proposed 
                         method is based on the estimation of a reference frame for the 
                         whole point cloud that represents an object instance, which is 
                         used for aligning it with the canonical coordinate system. After 
                         that, a descriptor is computed for the aligned point cloud based 
                         on how its 3D points are spatially distributed. Such descriptor is 
                         also extended with color distribution throughout the aligned point 
                         cloud. The global alignment transforms of matched point clouds are 
                         used for computing object pose. The proposed approach was 
                         evaluated with a publicly available dataset, showing that it 
                         outperforms major state of the art global descriptors regarding 
                         recognition rate and performance and that it allows precise pose 
                         estimation.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
                  doi = "10.1109/SIBGRAPI.2016.017",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2016.017",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M3QEQB",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M3QEQB",
           targetfile = "PID4349755.pdf",
        urlaccessdate = "2024, May 03"
}