author = "Longhurst, Peter and Debattista, Kurt and Gillibrand, Richard and 
                         Chalmers, Alan",
          affiliation = "{University of Bristol} and {Department of Computer Science} and 
                         {Merchant Venturers Building} and {Woodland Road} and Bristol. BS8 
                         1UB, UK",
                title = "Analytic antialiasing for selective high fidelity rendering",
            booktitle = "Proceedings...",
                 year = "2005",
               editor = "Rodrigues, Maria Andr{\'e}ia Formico and Frery, Alejandro 
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 18. 
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Antialiasing.",
             abstract = "Images rendered using global illumination algorithms are 
                         considered amongst the most realistic in 3D computer graphics. 
                         However, this high fidelity comes at a significant computational 
                         expense. A major part of this cost arises from the sampling 
                         required to eliminate aliasing errors. These errors occur due to 
                         the discrete sampling of continuous geometry space inherent to 
                         these techniques. In this paper we present a fast analytic method 
                         for predicting in advance where antialiasing needs to be computed. 
                         This prediction is based on a rapid visualisation of the scene 
                         using a GPU, which is used to drive a selective renderer. We are 
                         able to significantly reduce the overall number of anitialiasing 
                         rays traced, producing an image that is perceptually 
                         indistinguishable from the high quality image at a much reduced 
                         computational cost.",
  conference-location = "Natal",
      conference-year = "9-12 Oct. 2005",
             language = "en",
           targetfile = "longhurstp_antialiasing.pdf",
        urlaccessdate = "2021, Nov. 30"