@InProceedings{Netto:2023:RoPoRe,
               author = "Netto, Gustavo Marques",
          affiliation = "UFRGS",
                title = "Robust Point-Cloud Registration based on Dense Point Matching and 
                         Probabilistic Modeling",
            booktitle = "Proceedings...",
                 year = "2023",
               editor = "Clua, Esteban Walter Gonzalez and K{\"o}rting, Thales Sehn and 
                         Paulovich, Fernando Vieira and Feris, Rogerio",
         organization = "Conference on Graphics, Patterns and Images, 36. (SIBGRAPI)",
             keywords = "Point-cloud registration, rigid registration, non-rigid 
                         registration ,dense point matching.",
             abstract = "This thesis presents techniques for 3D point-cloud registration 
                         that are robust to outliers and missing regions. They tackle 
                         non-rigid and rigid registration and exploit the advantages of 
                         deep learning for dense point matching. This is done by proposing 
                         a single new neural network to solve both registration types. Our 
                         network uses a recently proposed attention mechanism and 
                         explicitly accounts for missing correspondences, which is key to 
                         its performance. Additionally, we use recent advances in 
                         probabilistic modeling to further refine the correspondences 
                         created by our network during non-rigid registration. Such a 
                         combination of deep learning and probabilistic modeling produces 
                         context awareness and enforces motion coherence, which makes our 
                         approach resilient to outliers and missing information. We 
                         demonstrate the effectiveness of our techniques by comparing them 
                         to state-of-the-art methods. Our comparisons use datasets 
                         containing noise, partial point clouds, and irregular sampling. 
                         The experiments show that our techniques obtain superior results 
                         in general. For example, our approaches achieve a registration 
                         error up to 45% smaller than other techniques in partial point 
                         clouds for non-rigid registration, and up to 49% smaller on rigid 
                         registration. We also discuss additional aspects of our techniques 
                         such as robustness to different levels of noise and to different 
                         numbers of samples in the point clouds. Finally, we tackle the 
                         lack of datasets with ground truth for supervised training of 
                         non-rigid registration models by presenting a self-supervised 
                         strategy based on random deformations.",
  conference-location = "Rio Grande, RS",
      conference-year = "Nov. 06-09, 2023",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/49SP3UH",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/49SP3UH",
           targetfile = "SIBGRAPI2023_Netto-1-1.pdf",
        urlaccessdate = "2024, May 02"
}