@InProceedings{Ortiz-FernandezSilvGonc:2022:ErAcEs,
               author = "Ortiz-Fernandez, Luis Enrique and da Silva, Bruno Marques Ferreira 
                         and Goncalves, Luiz Marcos Garcia",
          affiliation = "{Universidade Federal do Rio Grande do Norte} and {Universidade 
                         Federal do Rio Grande do Norte} and {Universidade Federal do Rio 
                         Grande do Norte}",
                title = "Error Accuracy Estimation of 3D Reconstruction and 3D Camera Pose 
                         from RGB-D Data",
            booktitle = "Proceedings...",
                 year = "2022",
         organization = "Conference on Graphics, Patterns and Images, 35. (SIBGRAPI)",
             keywords = "errors prediction, camera positioning, 3D reconstruction, RGB-D 
                         cameras.",
             abstract = "We propose an approach to predict accuracy for three-dimensional 
                         reconstruction and camera pose using a generic RGB-D camera on a 
                         robotic platform. We initially create a ground truth of 3D points 
                         and camera poses using a set of smart markers that we specifically 
                         devised and constructed for our approach. Then, we compute actual 
                         errors and their accuracy during the motion of our mobile robotic 
                         platform. The modeling of the error is then provided, which is 
                         used as input to a deep multi-layer perceptron in order to 
                         estimate accuracy as a function of the camera's distance, 
                         velocity, and vibration of the vision system. The network outputs 
                         are the root mean squared errors for the 3D reconstruction and the 
                         relative pose errors for the camera. Experimental results show 
                         that this approach has a prediction accuracy of 1 % for the 3D 
                         reconstruction and 2.5 % for camera poses, which shows a better 
                         performance in comparison with state-of-the-art methods.",
  conference-location = "Natal, RN",
      conference-year = "24-27 Oct. 2022",
                  doi = "10.1109/SIBGRAPI55357.2022.9991789",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI55357.2022.9991789",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/47M99TB",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/47M99TB",
           targetfile = "OrtizFernandezSIBGRAPI2022 (7).pdf",
        urlaccessdate = "2024, Sep. 08"
}