@InProceedings{SilveiraJung:2017:EvKeEx,
               author = "Silveira, Thiago Lopes Trugillo da and Jung, Cl{\'a}udio Rosito",
          affiliation = "{Federal University of Rio Grande do Sul - Institute of 
                         Informatics} and {Federal University of Rio Grande do Sul - 
                         Institute of Informatics}",
                title = "Evaluation of Keypoint Extraction and Matching for Pose Estimation 
                         using Pairs of Spherical Images",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "spherical images, keypoint detection, pose estimation.",
             abstract = "Keypoint extraction and matching has been widely studied by the 
                         computer vision community, mostly focused on pinhole camera 
                         models. In this paper we perform a comparative analysis of four 
                         keypoint extraction algorithms applied to full spherical images, 
                         particularly in the context of pose estimation. Two of the methods 
                         chosen for the comparative study, namely A-KAZE and ASIFT, have 
                         been designed considering a perspective camera model, but were 
                         already applied in an omnidirectional structure from motion 
                         pipeline, generating successful results in the literature. The 
                         other two algorithms are properly adapted versions of the 
                         traditional descriptors SIFT and ORB to the spherical domain, 
                         subbed SSFIT and SPHORB. We conduct our tests on captures of 
                         omnidirectional cameras, both synthetic and real, arbitrarily 
                         translated and rotated with known ground-truth transformations. 
                         The extracted keypoints are fed to the well-known 8-point 
                         algorithm with RANSAC, allowing to estimate the relative camera 
                         poses. These poses (translation vector and rotation matrix) are 
                         then compared to the ground-truth transformation parameters, 
                         generating the error metrics used in our analysis. Our results 
                         indicated that spherical descriptors SSIFT and SPHORB did not 
                         produce better results than planar descriptors A-KAZE and ASIFT in 
                         the context of pose estimation, particularly in the evaluation 
                         with real image pairs.",
  conference-location = "Niter{\'o}i, RJ, Brazil",
      conference-year = "17-20 Oct. 2017",
                  doi = "10.1109/SIBGRAPI.2017.56",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.56",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PF36SS",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3PF36SS",
           targetfile = "application.pdf",
        urlaccessdate = "2024, May 02"
}