author = "Hurtado, Jan and Gattass, Marcelo and Raposo, Alberto and Coelho, 
          affiliation = "{Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Rio de Janeiro} 
                         and {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Rio de 
                         Janeiro} and {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do 
                         Rio de Janeiro} and {Pontif{\'{\i}}cia Universidade 
                         Cat{\'o}lica do Rio de Janeiro}",
                title = "Adaptive patches for mesh denoising",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "adaptive patches, mesh denoising.",
             abstract = "The generation of triangular meshes typically introduces undesired 
                         noise which comes from different sources. Mesh denoising is a 
                         geometry processing task to remove this kind of distortion. To 
                         preserve the geometric fidelity of the desired mesh, a mesh 
                         denoising algorithm must maintain the object details while 
                         removing artificial high-frequencies from the surface. In this 
                         work, we propose a two-step algorithm which uses adaptive patches 
                         and bilateral filtering to denoise the normal vector field, and 
                         then update vertex positions fitting the faces to the denoised 
                         normals. The computation of the adaptive patches is our main 
                         contribution. We formulate this computation as local quadratic 
                         optimization problems that can be controlled by a set of 
                         parameters to obtain the desired behavior. We compared our 
                         proposal with several algorithms proposed in the literature using 
                         synthetic and real data. Our algorithm yields better results in 
                         general and is based on a formal mathematical formulation.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "en",
           targetfile = "PID5560825.pdf",
        urlaccessdate = "2020, Dec. 04"